Next Article in Journal
Loss of Flocculus Purkinje Cell Firing Precision Leads to Impaired Gaze Stabilization in a Mouse Model of Spinocerebellar Ataxia Type 6 (SCA6)
Previous Article in Journal
Mechanisms of Regulation in Intraflagellar Transport
Previous Article in Special Issue
Retinal Proteomic Alterations and Combined Transcriptomic-Proteomic Analysis in the Early Stages of Progression of a Mouse Model of X-Linked Retinoschisis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cells
Volume 11
Issue 17
10.3390/cells11172738
cells-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Small Nucleolar RNAs in Pseudoexfoliation Glaucoma

by
Karolina Gasińska
1,†,
Marcin Czop
2,†,
Ewa Kosior-Jarecka
1,*,
Dominika Wróbel-Dudzińska
1,
Janusz Kocki
2,‡ and
Tomasz Żarnowski
1,‡
1
Department of Diagnostics and Microsurgery of Glaucoma, Medical University of Lublin, 20-079 Lublin, Poland
2
Department of Clinical Genetics, Medical University of Lublin, 20-080 Lublin, Poland
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
These authors contributed equally to this work.
Cells 2022, 11(17), 2738; https://doi.org/10.3390/cells11172738
Submission received: 22 July 2022 / Revised: 24 August 2022 / Accepted: 31 August 2022 / Published: 2 September 2022
(This article belongs to the Special Issue New Advances in Cellular and Molecular Mechanisms Involved in Retinal Diseases)
Browse Figures
Versions Notes

Abstract

:
Small nucleolar RNAs (snoRNAs) are small non-coding regulatory RNAs that have been investigated extensively in recent years. However, the relationship between snoRNA and glaucoma is still unknown. This study aims to analyze the levels of snoRNA expression in the aqueous humor (AH) of patients with pseudoexfoliation glaucoma (PEXG) compared to a control group and identify hypothetical snoRNA-dependent mechanisms contributing to PEXG. The AH was obtained from eighteen Caucasian patients, comprising nine PEXG and nine age-matched control patients. RNA was isolated, and a microarray system was used to determine the snoRNA expression profiles. Functional and enrichment analyses were performed. We identified seven snoRNAs, SNORD73B, SNORD58A, SNORD56, SNORA77, SNORA72, SNORA64, and SNORA32, in the AH of the PEXG and control group patients. Five snoRNAs showed statistically significantly lower expression in the PEXG group, and two snoRNAs had statistically significantly higher expression in the PEXG group compared to the control group. In addition, we identified two factors—CACNB3 for SNORA64 and TMEM63C for SNORA32, similar to PEX-related genes (CACNA1A and TMEM136). The enrichment analysis for four genes targeted by snoRNAs revealed possible mechanisms associated with glaucoma and/or PEX, but the direct role of snoRNAs in these biological processes was not proven.

1. Introduction

Pseudoexfoliation syndrome (PEX) is an age-related systemic disease that affects 10–20% of the world’s population over the age of 60 [1]. The incidence rate varies geographically and ethnically. PEX is most common in Nordic, Baltic, Mediterranean, and Arab countries [2]. It is manifested by the deposition of abnormal gray–white material in eye tissues bathed by the aqueous humor (AH) and in the blood vessels, heart, lungs, liver, kidneys, and skin. PEX material is derived from the extracellular matrix (ECM) and contains elastic fibers composed of fibrillin-1, fibulin-2, vitronectin, lysyl oxidase, clusterin, and other proteins. Transforming growth factor beta 1 (TGF-β1) has been found to interact with lysyl oxidase in the formation of elastic fibers and is upregulated in anterior segment tissues in PEX [3]. Mechanisms leading to the development of PEX are complex and may include oxidative stress, hypoperfusion, and hypoxia [4]. PEX is associated with the development of pseudoexfoliation glaucoma (PEXG) and an increased risk of cardiovascular as well as cerebrovascular disorders [5]. In addition, Alzheimer’s disease, a progressive neurodegenerative condition in which extracellular amyloid plaques build up in the brain’s gray matter, is more common in PEX patients [6,7]. The evidence points to a strong genetic component of PEX. It is associated with single-nucleotide polymorphisms (SNPs) of numerous genes, including LOXL1, FBLN5, CLU, GST, CNTNAP2, CACNA1A, and TMEM136 (also known as TLCD5) [3,4,5,6,7,8,9].
PEXG is the most common type of secondary glaucoma [10]. As with the other types of glaucoma, elevated intraocular pressure (IOP) is a major cause of retinal ganglion cell (RGC) apoptosis [11]. The key structural feature of the eye in the regulation of IOP is the human trabecular meshwork (HTM), which consists of trabecular cells surrounded by the ECM. The ECM has the greatest share in the production of resistance to AH outflow, and the disturbance of its homeostasis leads to an increase in IOP [12]. In PEXG, IOP often reaches high maximum values, and the large coexisting fluctuations in IOP also accelerate the progression of optic nerve degeneration [10,13]. However, as indicated in the long-term observations of patients, PEX material in the eyes does not always lead to increased IOP and glaucoma, or glaucoma occurs despite normal IOP [14,15]. This is evidence of complex mechanisms involved in the pathogenesis of PEXG and individually variable susceptibility to optic nerve damage. Therefore, the participation of genetic factors in determining these processes appears crucial.
The glaucoma phenotype is determined not only by genes themselves but also by regulatory factors that influence the expression of these genes. The regulatory function may be performed by small non-coding RNA molecules, including microRNAs (miRNAs) and small nucleolar RNAs (snoRNAs). Many studies have uncovered extensive similarities between miRNA and snoRNA [16]. MiRNA is present in the AH, and its frequency and expression vary in different types of glaucoma [17]. However, there are no similar studies on snoRNA.
snoRNA molecules are some of the oldest in the evolution of non-protein-coding RNAs [18]. They are composed of 60–300 nucleotides and are located in the nucleolus—a nucleus region with high protein density [19,20]. A number of studies suggest that snoRNA localization to the nucleus is dynamic and regulated, and that it can relocalize to the cytoplasm [21]. Additionally, snoRNAs form specific complexes with proteins called small nucleolar ribonucleoproteins (snoRNPs). They are involved in the chemical modification of ribosomal RNA (rRNA) and small nuclear RNA (snRNA) nucleotides, and participate in rRNA maturation. SnoRNAs are classified into either C/D (SNORD) or H/ACA box (SNORA) subfamilies. C/D box snoRNAs catalyze 2′-O-ribose methylation and H/ACA box snoRNAs—pseudouridylation [22,23].
Emerging evidence demonstrates that snoRNAs are involved in various physiological and pathological cellular processes. Currently, only limited studies have reported the role of snoRNAs in ocular diseases. In an oxygen-induced proliferative retinopathy model in mouse retinas, 19 snoRNAs had significantly altered expression levels [24]. Another study on the expression profiles of snoRNAs in uveal melanoma (UM) showed 60 snoRNAs notably associated with the overall survival of UM patients [25]. Additionally, mutations and the aberrant expression of snoRNAs are reported in cell transformation, tumorigenesis, and metastasis, indicating that snoRNAs may serve as biomarkers and/or therapeutic targets of cancer [19]. For instance, by causing changes in ribosomal subunits (18S rRNA, 28S rRNA), snoRNAs are associated with various types of leukemia and cancers [26]. Additionally, a link between snoRNAs and Prader–Willi syndrome—a complex human neurological disease involving mental retardation, low height, obesity, and muscle hypotonia—has been found [27]. Furthermore, snoRNAs are reported to be differentially expressed in multiple sclerosis, a common inflammatory and degenerative disease that causes neurological disability [28].
As the relationship between snoRNA and glaucoma remains unclear, this study aimed to analyze the level of snoRNA expression in the AH of patients with PEXG compared to a control group as well as identify hypothetical snoRNA-dependent mechanisms contributing to PEXG.

2. Materials and Methods

2.1. Study Groups

In the present study, the AH was obtained from 18 Caucasian patients, comprising of nine PEXG and nine age-matched control patients (eight men and one woman in each group) who underwent routine cataract surgery at the Department of Diagnostics and Microsurgery of Glaucoma, Medical University of Lublin (Poland). All of the participants were over 18. Before the surgery, all of the patients underwent a comprehensive ophthalmic examination and medical history analysis. The ophthalmic examination included 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). Two groups of patients were categorized, as are expanded upon below:
(I).
Patients with incipient senile cataract and an advanced stage of PEXG: (a) cloudy area in the lens that causes decreased vision; (b) elevated IOP of greater than 21 mmHg, documented in medical history; (c) open-angle grade III/IV according to Schaffer’s classification; (d) glaucomatous optic nerve head damage (excavation, neuroretinal rim thinning or notching, and localized or diffuse retinal nerve fiber layer (RNFL) defect); (e) 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 one point at p < 0.5%); and (f) typical deposits of pathological material observed, during a slit-lamp examination, as concentric rings of grayish fringes on the peripheral lens capsule and flakes on the pupillary border of the iris.
(II).
Control patients with incipient senile cataract: (a) cloudy area in the lens that causes decreased vision; (b) normal IOP (from 10 mmHg to 21 mmHg); and (c) no clinical signs of glaucoma nor PEX.
In all of the patients in Groups (I) and (II), 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 after a careful medical history analysis, ophthalmologic examination, and OCT evaluation, including RNFL measurements and disc topography parameters. Moreover, all of the participants in Groups (I) and (II) 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

AH samples (approximately 100 µL) 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 snoRNA Profiling

A microarray system (GeneChip miRNA 4.0 Array chip, Affymetrix, Santa Carla, CA, USA) was used to determine the snoRNA 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 snoRNAs 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. Functional and Enrichment Analysis

The data from TAC were used to create a snoRNA list. The analysis of the interactions of the selected snoRNAs with genes was carried out in the snoDB database [29]. The enrichment analysis was performed using the mirNET database, where a list of selected snoRNAs and a list of genes with which they interact were introduced [30]. Terms of the Kyoto Encyclopedia of Genes and Genomes (KEGG), REACTOME, and Gene Ontology (GO) categories were searched for in PEX-related pathways.

2.6. Statistical Analysis

Graph Pad Prism 7 (Graph Pad Software, San Diego, CA, USA) and Statistica 13.3 (StatSoft, Krakow, Poland) were employed to prepare the Student’s t-test, chi2 test, and ROC curves. The distribution of the analyzed data was consistent with the normal distribution (Shapiro–Wilk test).

3. Results

Seven snoRNAs were detected as DEGs in the AH of the 18 Caucasian patients from the PEXG and control groups—SNORD73B, SNORD58A, SNORD56, SNORA77, SNORA72, SNORA64, and SNORA32. The list of detected snoRNAs along with their interactions are provided in Table 2.
In the aqueous humor, we detected three C/D box snoRNAs (SNORD73B, SNORD58A, and SNORD56) and four H/ACA box snoRNAs (SNORA77, SNORA72, SNORA64, and SNORA32). Our analysis shows that snoRNAs interact with genes and other snoRNAs. These interactions are complex, and a single snoRNA molecule can act on many targets. For the seven selected snoRNAs, we found four target genes and three target snoRNAs. The above-mentioned interactions between snoRNAs and protein genes have been previously suggested in studies: Aw et al. (2016), Kretz et al. (2013), and Lu et al. (2016) [31,32,33]. SnoRNA host genes mainly encode proteins with a broad spectrum of biological roles, and three of them are RNA genes (AC012501.2, LINC02463, and PLS3-AS1) (Table 2).
We revealed target genes for two out of the seven snoRNAs. The SNORD56 molecule affects FAM120AOS (in the 3′UTR region), which encodes uncharacterized protein FAM120AOS (family with sequence similarity 120A opposite strand). The SNORA64 molecule regulates RPS2 (interaction in the coding sequence of the gene) encoding ribosomal protein S2, which is a component of the 40S subunit, and also TNPO2 (transportin-2) (interaction in the 3′UTR region) as well as CACNB3 (interaction in the coding sequence of the gene) (regulatory subunit beta-3 of the voltage-dependent calcium channel) (Table 2 and Table 3).
The SNORD56 molecule interacts with the SNORA7B molecule, and their common host gene is NOP56. The second interaction is between SNORD56 and SNORA65 or SNORD50A molecules, and their common host gene is SGSM1 (Table 2).
By analyzing the results of snoRNA expression [log2], we found that five out of the seven snoRNAs—SNORA32, SNORD56, SNORA64, SNORA72, and SNORD73B—had lower expression in the PEXG group compared to the cataract group (p-value < 0.01; AUC ≥ 0.870; and AUC p-value < 0.01). SNORA32 was 16.42% lower (0.235), SNORD56 was 22.98% lower (0.313), SNORA64 was 13.14% lower (0.151), SNORA72 was 17.72% lower (0.218), and SNORD73B was 15.46% lower (0.216). The mean expression for all of the snoRNAs was 1.3138 in the cataract group and 1.0872 in the PEXG group, i.e., the mean decrease in expression level was 17.25% (0.2266). These data are shown in Table 4 and Figure 1.
Two out of the seven snoRNAs—SNORD58A and SNORA77—had a higher expression level in the PEXG group compared to the cataract group (p-value < 0.01; AUC ≥ 0.815; and AUC p-value < 0.03). SNORD58A was 15.94% higher (0.193), and SNORA77 was 25.68% higher (0.273). The mean expression for all of the snoRNAs was 1.137 in the cataract group and 1.370 in the PEXG group, i.e., the mean increase in expression level was 20.49% (0.233). These data are shown in Table 5 and Figure 2.
We performed a cluster analysis based on the expression levels of the detected snoRNAs; the results are presented in Figure 3.
To indicate biological processes involving snoRNA-regulated genes, an enrichment analysis was carried out using the mirNET database for four genes: CACNA3, FAM120AOS, RPS2, and TNPO2. We selected four categories: GO: Biological Processing (GO:BP), GO: Cellular Compartment (GO:CC), GO: Molecular Function (GO:MF), and REACTOME. Genes indicated as targets of the expressed snoRNAs were associated with intracellular and extracellular structures in addition to various processes inside and outside cells. Selected data that may be potentially related to glaucoma and/or PEX are presented in Figure 4. In GO:BP, we selected eight terms; in GO:CC, five; in GO:MF, twelve; and in REACTOME, two, i.e., twenty-seven terms in total. The most significant glaucoma- and/or PEX-associated terms concerned the ion signaling pathway (especially calcium) and protein transport. Explanations of terms of the GO:BP, GO:CC, GO:MF, and REACTOME categories are presented in Table S1.

4. Discussion

Although snoRNAs have been known since the late 1960s, and the term “snoRNA” has been used since 1981 [34], their involvement in physiological and pathological processes remains poorly understood. This may be due to a considerable diversity in non-coding RNAs, which interact with one another and together regulate gene expression. A large proportion of snoRNAs are further processed into smaller molecules, some of which display different functionalities. Comparing the genomic locations of snoRNAs and miRNAs reveals an overlap of specific members of these classes. Both groups have members encoded in introns and others encoded in independent transcription units. Furthermore, the proportion of intronic snoRNAs generally correlates with the proportion of intronic miRNAs. In addition, there are similarities in the processing pathways of snoRNAs and miRNAs [16,35].
Several protein-coding host genes encode both snoRNAs and miRNAs in distinct introns. For example, NOP56 (locus 20p13), found as the target gene for SNORD56, detected in our study, encodes the box C/D snoRNAs SNORD56, SNORD57, SNORD86, and SNORD110, the box H/ACA snoRNA SNORA51, and the miRNA miR-1292. Additionally, HTR2C (locus Xq23), which has a similar locus as PLS3-AS1 (locus Xq23), found as the target gene for SNORA64, detected in our study, encodes the box H/ACA snoRNA SNORA35 as well as the miRNAs miR-448, miR-1264, miR-1298, miR-1911, and miR-1912 [16].
One study shows almost 400 different snoRNAs in the murine retina and RPE/choroid. SNORD27 was the most abundant in the retina, while SNORD58 was in the RPE/choroid (85-fold higher than in the retina). In addition, snoRNAs can be processed to generate snoRNA-derived small RNAs that resemble microRNAs (sno-miRNAs). Many of these molecules have also been detected in the retina and RPE/choroid [36].
The role of snoRNAs is crucial in genetic diseases because it can lead to a better understanding of their underlying mechanisms and establish potential RNA-based biomarkers in addition to future therapies. One of the most important eye diseases with a genetic background is glaucoma—a common cause of blindness worldwide. PEXG is a type of secondary open-angle glaucoma that accompanies PEX, a systemic disease manifesting primarily in the eyes via the accumulation of pathological material in the anterior segment. The prognosis of PEXG is often worse than that of primary open-angle glaucoma (POAG), wherein patients may present with a more severe clinical course, a poorer response to medications, and a more frequent necessity for surgical intervention. The mechanisms leading to the development of glaucomatous neuropathy are complex and not fully understood. The main risk factor is increased IOP caused by the impaired outflow of AH from the anterior chamber through the HTM [13]. Oxidative stress occurring in the anterior chamber damages the HTM, contributing to an increase in IOP, and in the retina may directly cause the apoptosis of RGCs. Moreover, ocular blood supply disorders play an important role [37]. Additionally, the potential genetical tendency of RGCs for excessive apoptosis is also kept in mind.
Some researchers investigated the role of small nucleolar RNA host gene 3 (SNHG3, locus 1p35.3) in HTM cells under oxidative stress. Hydrogen peroxide (H2O2) induces an acute oxidative stress injury in HTM cells to create a glaucoma model. They observed H2O2-induced SNHG3 upregulation in HTM cells. SNHG3 silencing alleviated H2O2-induced oxidative damage in HTM cells. Moreover, SNHG3 cooperated with ELAV-like RNA-binding protein 2 (ELAVL2) to modulate cell apoptosis and ECM accumulation in HTM cells under oxidative stress [38]. Similar findings have been reported in another study using a model of H2O2-induced oxidative stress in HTM cells. Oxidative stress conditions in HTM cells significantly upregulated SNORD3A [39].
Furthermore, one study investigated snoRNA expression in developing mouse lenses. As a result, 167 snoRNAs were found in total—113 C/D box snoRNAs and 54 H/ACA box snoRNAs. Notably, the upregulation of snoRNAs in the postnatal mouse lens transcriptome was observed [40]. In our study, we chose patients with incipient senile cataract without other ocular diseases as a control group; however, the impact of cataract on the snoRNA expression results in the PEXG group should be taken into consideration. As the role of snoRNA-dependent pathways in cataract pathogenesis is currently unknown, this is a major limitation of our study. On the other hand, there is no ethically approved possibility to obtain the AH from healthy individuals, and senile cataract appears in almost every older patient at different stages.
Our work demonstrated that in the AH of patients with PEXG and control cataract, seven snoRNAs, SNORD73B, SNORD58A, SNORD56, SNORA77, SNORA72, SNORA64, and SNORA32, can be identified, including statistically significant differences in expression levels. Five snoRNAs, SNORA32, SNORD56, SNORA64, SNORA72, and SNORD73B, had lower expression in the PEXG group, and two snoRNAs, SNORD58A and SNORA77, had higher expression in the PEXG group compared to the cataract group. The differences in snoRNA expression were small; therefore, based on this information, we cannot conclude that they have a measurable effect on the function of their targets. Moreover, patients in our study had advanced PEXG, which suggests that snoRNA molecules may be the result of the disease process and not a causative factor, and they cannot be considered as biomarkers. A comparison of snoRNAs in the early stages of PEXG is needed.
The study that we conducted is the first attempt to discover the types of snoRNA present in the AH of patients with PEXG and PEX. Additionally, there are no studies showing snoRNA profiles in the AH in any other diseases. The origin of snoRNA in the AH is not clear. They may be derived from exosomes that are a major constituent of AH. Exosomes function in extracellular communication and contain proteins as well as small RNA [41]. Another reason for the presence of snoRNA in the AH may be surgical procedures that cause blood–aqueous barrier disruption with the leakage of cells in the anterior chamber [42]. snoRNA could also be released from damaged corneal endothelium cells. The AH is a unique material, available in small amounts, and can only be obtained intraoperatively.
The snoRNAs identified in our study have not been described in other ocular diseases. In one study, SNORD58 was detected in the RPE/choroid at a very high expression level. The exact function of this snoRNA is unknown, but the most highly expressed sno-miRNAs in this localization are implicated in the maintenance of the blood–retinal barrier and in promoting RPE differentiation [36]. In diseases not related to the eyes, SNORD56 was upregulated in uterine leiomyoma [43]. Additionally, SNORA72 was found to be highly expressed in ovarian sphere cells. The overexpression of SNORA72 increased ovarian cancer cells’ self-renewal and migration abilities [44]. Furthermore, SNORA64 expression was increased in multiple myeloma, prostate cancer, and X-linked dyskeratosis congenita [26].
Our study identified four target genes and three target snoRNAs for the investigated snoRNA molecules. None of these findings have previously been associated with PEXG and PEX. However, CACNB3 (targeted by SNORA64) is similar to a PEX-associated gene—CACNA1A.
Calcium channels play an important role in PEX pathogenesis because a high calcium concentration in direct association with aggregating PEX fibrils was detected. One of the components of PEX material is fibrillin, which utilizes calcium to form stable aggregates; therefore, the altered function of a calcium channel could facilitate the formation of PEX aggregates [8]. Calcium channels are responsible for the transport of calcium ions across the cell membrane. They are multisubunit complexes composed of alpha-1, beta, and alpha-2/delta subunits. The channel activity is directed by the pore-forming alpha-1 subunit, whereas the others act as auxiliary subunits regulating this activity [45]. The CACNA1A (locus 19p13.13) encodes the alpha-1A subunit of the voltage-gated calcium channel and is expressed in the eye in the anterior lens epithelium, iris, ciliary body, optic nerve glia, and vascular endothelial cells [8]. CACNB3 (locus 12q13.12) encodes the regulatory subunit beta-3 of the voltage-gated calcium channel. Beta subunits are composed of five domains, which contribute to the regulation of surface expression and the gating of calcium channels, and may also regulate gene transcription [46,47,48]. It has been proven that calcium channels may participate in PEX material formation by increasing the calcium concentration [8]. Moreover, one of the studies showed that the visual pathway development is disrupted in mice with a targeted disruption of the CACNB3 gene [49]. In a rat model of cerebral ischemia, CACNB3, which might represent functional modules within the ischemic neuronal transcriptome, was induced [50]. In bipolar disorder, the elevation of miR-34a expression in human neuronal progenitors affected CACNB3 mRNA and protein expression, and resulted in defects in neuronal differentiation [51].
As the host genes of snoRNAs have been shown to be abnormally expressed in multiple cancers [52], they could also have an impact on other diseases. In Table 2, we presented the SNORA32 host gene, TMEM63C, which shows similarity to the TMEM136 gene described in PEX.
A pronounced and early vasculopathy, partly involving PEX material deposits around ocular blood vessels, appears to play a significant role in PEX [9]. Vasculopathy is caused by the dysregulation of endothelial cell function within the vascular wall, which leads to vascular remodeling [53]. Cell stabilization is provided by transmembrane proteins, which participate in the transport of ions and other substances across the cell membrane and are the basis of most cell surface receptors [54]. TMEM136 (locus 11q23.3) encodes transmembrane protein 136, which was found to be primarily localized to the endothelial cells of blood vessels and aqueous outflow structures, and may contribute to vascular and trabecular dysfunction in PEX eyes. The role of TMEM136 remains unclear, but it seems to maintain the proper functioning of cells by regulating cellular potassium ion transport. Some studies have shown significantly reduced expression of TMEM136 in iris and ciliary body tissues in PEX patients [3,9,55,56]. TMEM63C (locus 14q24.3) encodes transmembrane protein 63C, which enables calcium-activated cation channel activity and is involved in cation transport [57,58]. Calcium participates in regulating voltage-gated sodium, calcium, and potassium channels [59]. Calcium and potassium channels may be relevant in PEX development [3,8]. Recent studies show the possible role of transient receptor potential (TRP) channels in glaucoma. They are a cluster of non-selective cation channels present on cell membranes. Various TRP subfamilies are abundantly expressed in ocular structures, including the cornea, lens, ciliary body, HTM, and retina. They are involved in AH secretion and cytoskeleton remodeling of the HTM to control IOP by affecting retinal function [60]. However, there is no clear evidence for the involvement of TRP channels in the pathogenesis of PEX. In a study on age-related macular degeneration, a suggestive association signal in TMEM63C was found, but its role in AMD pathogenesis is unknown [61]. Additionally, TMEM63C is considered a biomarker of focal segmental glomerulosclerosis (FSGS) [57,62]. FSGS shows the obliteration or collapse of glomerular capillary loops via increased ECM [63]. Similar changes in blood vessels are observed in PEX patients, as PEX is frequently linked to increased vascular resistance and decreased blood flow velocity, arterial endothelial dysfunction, and arterial hypertension. This could imply that PEX and FSGS have common features in their pathogenesis [5].
To reveal the predicted associations with the biological processes, we performed enrichment analyses for four genes, CACNA3, FAM120AOS, RPS2, and TNPO2, targeted by SNORA32, SNORA64, SNORA72, SNORA77, SNORD58A, and SNORD73B, using the mirNET database. However, based on these data, we cannot prove that actual interactions between snoRNAs and their predicted targets exist because of a lack of experimental evidence for such correlations. On the other hand, these interactions have been suggested in previous studies: Aw et al. (2016), Kretz et al. (2013), and Lu et al. (2016) [31,32,33]. Terms associated with PEX pathogenesis include protein transport, protein localization, protein targeting to membranes, ribosomes (ribonucleoprotein complexes responsible for protein synthesis in cells), calcium channel activity, and growth factor binding. Simultaneously, impaired protein expression, the dysfunction of calcium channels, and TGF accumulation are well-known as glaucoma-associated factors.
During the onset of glaucoma, the elevation in IOP causes ischemia, hypoxia, and oxidative stress accumulation in the retina, all of which contribute to the apoptosis of RGCs [60]; AH outflow resistance is determined in particular by the ECM [64]. Along with increased protein deposition (mainly collagen and elastin), the ECM becomes more dense and stiff, which leads to an increase in IOP [65]. Many ECM-stimulating factors are found to be upregulated in glaucoma, including TGF-β2 in the AH and actin secreted by trabecular cells [65,66]. Additionally, IOP is regulated by HTM cell contraction (nitric oxide) [67], the cytoskeleton density of trabecular and Schlemm’s canal cells (RhoA protein) [68,69], and by transmembrane proteins that ensure the calcium balance as well as participate in the adhesion of these cells (integrins) [70]. Increased IOP results in retinal blood flow disorder and, therefore, antioxidant imbalance, leading to the death of RGCs [71].

5. Conclusions

The study that we conducted is the first attempt to discover the types of snoRNA present in the AH of patients with PEXG and PEX. We identified seven snoRNAs: five snoRNAs, SNORA32, SNORD56, SNORA64, SNORA72, and SNORD73B, had statistically significant lower expression in the PEXG group (mean decrease of 17.25%), and two snoRNAs, SNORD58A and SNORA77, showed statistically significant higher expression in the PEXG group (mean increase of 20.49%) compared to the control group. However, this small change in snoRNA expression may not be functionally significant.
Moreover, we found two factors, the CACNB3 target gene for SNORA64 and the TMEM63C host gene for SNORA32, that could be potentially important in PEX pathogenesis, as they have similar functions to the CACNA1A and TMEM136 genes in PEX. In addition, the enrichment analysis for four genes targeted by snoRNAs revealed possible mechanisms associated with glaucoma and/or PEX, but the direct role of snoRNAs in these biological processes is not proven. Further research is needed to better understand the functional diversity of snoRNA molecules and their role in PEXG.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cells11172738/s1, Table S1: Explanations of terms of the Gene Ontology (GO): Biological Processing (GO:BP), GO: Cellular Compartment (GO:CC), GO: Molecular Function (GO:MF), and REACTOME categories.

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 (and its Supporting Information File).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lesiewska, H.; Łukaszewska-Smyk, A.; Odrowąż-Sypniewska, G.; Krintus, M.; Mańkowska-Cyl, A.; Malukiewicz, G. Chosen Vascular Risk Markers in Pseudoexfoliation Syndrome: An Age-Related Disorder. J. Ophthalmol. 2017, 2017, 5231095. [Google Scholar] [CrossRef]
  2. Schlotzer-Schrehardt, U. Genetics and Genomics of Pseudoexfoliation Syndrome/Glaucoma. Middle East Afr. J. Ophthalmol. 2011, 18, 30. [Google Scholar] [CrossRef]
  3. 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]
  4. Tuteja, S.; Chawla, H. Pseudoexfoliation Syndrome; StatPearls: Treasure Island, FL, USA, 2022. [Google Scholar]
  5. Andrikopoulos, G.K.; Alexopoulos, D.K.; Gartaganis, S.P. Pseudoexfoliation Syndrome and Cardiovascular Diseases. World J. Cardiol. 2014, 6, 847–854. [Google Scholar] [CrossRef]
  6. Cumurcu, T.; Dorak, F.; Cumurcu, B.E.; Erbay, L.G.; Ozsoy, E. Is There Any Relation Between Pseudoexfoliation Syndrome and Alzheimer’s Type Dementia? Semin. Ophthalmol. 2013, 28, 224–229. [Google Scholar] [CrossRef]
  7. Nasrabady, S.E.; Rizvi, B.; Goldman, J.E.; Brickman, A.M. White Matter Changes in Alzheimer’s Disease: A Focus on Myelin and Oligodendrocytes. Acta Neuropathol. Commun. 2018, 6, 22. [Google Scholar] [CrossRef]
  8. 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]
  9. 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, 993–1004. [Google Scholar] [CrossRef]
  10. 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, 676579. [Google Scholar] [CrossRef]
  11. Liu, Y.; Allingham, R.R. Molecular Genetics in Glaucoma. Exp. Eye Res. 2011, 93, 331–339. [Google Scholar] [CrossRef] [Green Version]
  12. 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] [PubMed]
  13. 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] [PubMed]
  14. Łukasik, U.; Kosior-Jarecka, E.; Wróbel-Dudzińska, D.; Kustra, A.; Milanowski, P.; Żarnowski, T. Clinical Features of Pseudoexfoliative Glaucoma in Treated Polish Patients. Clin. Ophthalmol. 2020, 14, 1373–1381. [Google Scholar] [CrossRef] [PubMed]
  15. Koz, O.G.; Turkcu, M.F.; Yarangumeli, A.; Koz, C.; Kural, G. Normotensive Glaucoma and Risk Factors in Normotensive Eyes with Pseudoexfoliation Syndrome. J. Glaucoma 2009, 18, 684–688. [Google Scholar] [CrossRef]
  16. Scott, M.S.; Ono, M. From SnoRNA to MiRNA: Dual Function Regulatory Non-Coding RNAs. Biochimie 2011, 93, 1987–1992. [Google Scholar] [CrossRef] [PubMed]
  17. 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. Graefes Arch. Clin. Exp. Ophthalmol. 2021, 259, 2337–2349. [Google Scholar] [CrossRef]
  18. Patra Bhattacharya, D.; Canzler, S.; Kehr, S.; Hertel, J.; Grosse, I.; Stadler, P.F. Phylogenetic Distribution of Plant SnoRNA Families. BMC Genom. 2016, 17, 969. [Google Scholar] [CrossRef]
  19. Liang, J.; Wen, J.; Huang, Z.; Chen, X.; Zhang, B.; Chu, L. Small Nucleolar RNAs: Insight into Their Function in Cancer. Front. Oncol. 2019, 9, 587. [Google Scholar] [CrossRef]
  20. Kobayashi, T. A New Role of the RDNA and Nucleolus in the Nucleus—RDNA Instability Maintains Genome Integrity. BioEssays 2008, 30, 267–272. [Google Scholar] [CrossRef]
  21. Rimer, J.M.; Lee, J.; Holley, C.L.; Crowder, R.J.; Chen, D.L.; Hanson, P.I.; Ory, D.S.; Schaffer, J.E. Long-Range Function of Secreted Small Nucleolar RNAs That Direct 2′-O-Methylation. J. Biol. Chem. 2018, 293, 13284–13296. [Google Scholar] [CrossRef] [Green Version]
  22. Ellis, J.C.; Brown, D.D.; Brown, J.W. The Small Nucleolar Ribonucleoprotein (SnoRNP) Database. RNA 2010, 16, 664–666. [Google Scholar] [CrossRef] [PubMed]
  23. Bratkovič, T.; Božič, J.; Rogelj, B. Functional Diversity of Small Nucleolar RNAs. Nucleic Acids Res. 2020, 48, 1627–1651. [Google Scholar] [CrossRef]
  24. Liu, C.-H.; Wang, Z.; Sun, Y.; SanGiovanni, J.P.; Chen, J. Retinal Expression of Small Non-Coding RNAs in a Murine Model of Proliferative Retinopathy. Sci. Rep. 2016, 6, 33947. [Google Scholar] [CrossRef] [PubMed]
  25. Yi, Q.; Zou, W. A Novel Four-snoRNA Signature for Predicting the Survival of Patients with Uveal Melanoma. Mol. Med. Rep. 2019, 19, 1294–1301. [Google Scholar] [CrossRef] [PubMed]
  26. McMahon, M.; Contreras, A.; Ruggero, D. Small RNAs with Big Implications: New Insights into H/ACA SnoRNA Function and Their Role in Human Disease. Wiley Interdiscip. Rev. RNA 2015, 6, 173–189. [Google Scholar] [CrossRef]
  27. Dimitrova, D.G.; Teysset, L.; Carré, C. RNA 2′-O-Methylation (Nm) Modification in Human Diseases. Genes 2019, 10, E117. [Google Scholar] [CrossRef]
  28. Muñoz-Culla, M.; Irizar, H.; Sáenz-Cuesta, M.; Castillo-Triviño, T.; Osorio-Querejeta, I.; Sepúlveda, L.; López de Munain, A.; Olascoaga, J.; Otaegui, D. SncRNA (MicroRNA &snoRNA) Opposite Expression Pattern Found in Multiple Sclerosis Relapse and Remission Is Sex Dependent. Sci. Rep. 2016, 6, 20126. [Google Scholar] [CrossRef]
  29. snoDB: A Database of Human snoRNA. Available online: http://scottgroup.med.usherbrooke.ca/snoDB/ (accessed on 22 June 2022).
  30. miRNet–a miRNA-Centric Network Visual Analytics Platform. Available online: https://www.mirnet.ca/miRNet/home.xhtml (accessed on 22 June 2022).
  31. Aw, J.G.A.; Shen, Y.; Wilm, A.; Sun, M.; Lim, X.N.; Boon, K.-L.; Tapsin, S.; Chan, Y.-S.; Tan, C.-P.; Sim, A.Y.L.; et al. In Vivo Mapping of Eukaryotic RNA Interactomes Reveals Principles of Higher-Order Organization and Regulation. Mol. Cell 2016, 62, 603–617. [Google Scholar] [CrossRef]
  32. Kretz, M.; Siprashvili, Z.; Chu, C.; Webster, D.E.; Zehnder, A.; Qu, K.; Lee, C.S.; Flockhart, R.J.; Groff, A.F.; Chow, J.; et al. Control of Somatic Tissue Differentiation by the Long Non-Coding RNA TINCR. Nature 2013, 493, 231–235. [Google Scholar] [CrossRef]
  33. Lu, Z.; Zhang, Q.C.; Lee, B.; Flynn, R.A.; Smith, M.A.; Robinson, J.T.; Davidovich, C.; Gooding, A.R.; Goodrich, K.J.; Mattick, J.S.; et al. RNA Duplex Map in Living Cells Reveals Higher-Order Transcriptome Structure. Cell 2016, 165, 1267–1279. [Google Scholar] [CrossRef] [Green Version]
  34. Bertrand, E.; Maurille, M.J. The SnoRNPs and Related Machines: Ancient Devices That Mediate Maturation of RRNA and Other RNAs. In The Nucleolus; Olson, M.O.J., Ed.; Landes Bioscience: Austin, TX, USA, 2004; pp. 225–261. [Google Scholar]
  35. Scott, M.S.; Avolio, F.; Ono, M.; Lamond, A.I.; Barton, G.J. Human MiRNA Precursors with Box H/ACA SnoRNA Features. PLoS Comput. Biol. 2009, 5, e1000507. [Google Scholar] [CrossRef] [PubMed]
  36. Soundara Pandi, S.P.; Chen, M.; Guduric-Fuchs, J.; Xu, H.; Simpson, D.A. Extremely Complex Populations of Small RNAs in the Mouse Retina and RPE/Choroid. Investig. Ophthalmol. Vis. Sci. 2013, 54, 8140. [Google Scholar] [CrossRef] [PubMed]
  37. Izzotti, A.; Bagnis, A.; Sacca, S. The Role of Oxidative Stress in Glaucoma. Mutat. Res. 2006, 612, 105–114. [Google Scholar] [CrossRef] [PubMed]
  38. Li, S.; Yang, Q.; Zhou, Z.; Fu, M.; Yang, X.; Hao, K.; Liu, Y. SNHG3 Cooperates with ELAVL2 to Modulate Cell Apoptosis and Extracellular Matrix Accumulation by Stabilizing SNAI2 in Human Trabecular Meshwork Cells under Oxidative Stress. Environ. Toxicol. 2021, 36, 1070–1079. [Google Scholar] [CrossRef]
  39. 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]
  40. Khan, S.Y.; Hackett, S.F.; Riazuddin, S.A. Non-Coding RNA Profiling of the Developing Murine Lens. Exp. Eye Res. 2016, 145, 347–351. [Google Scholar] [CrossRef]
  41. Dismuke, W.M.; Challa, P.; Navarro, I.; Stamer, W.D.; Liu, Y. Human Aqueous Humor Exosomes. Exp. Eye Res. 2015, 132, 73–77. [Google Scholar] [CrossRef] [PubMed]
  42. De Maria, M.; Iannetta, D.; Cimino, L.; Coassin, M.; Fontana, L. Measuring Anterior Chamber Inflammation After Cataract Surgery: A Review of the Literature Focusing on the Correlation with Cystoid Macular Edema. Clin. Ophthalmol. 2020, 14, 41–52. [Google Scholar] [CrossRef]
  43. Chuang, T.-D.; Xie, Y.; Yan, W.; Khorram, O. Next-Generation Sequencing Reveals Differentially Expressed Small Noncoding RNAs in Uterine Leiomyoma. Fertil. Steril. 2018, 109, 919–929. [Google Scholar] [CrossRef]
  44. Zhang, L.; Ma, R.; Gao, M.; Zhao, Y.; Lv, X.; Zhu, W.; Han, L.; Su, P.; Fan, Y.; Yan, Y.; et al. SNORA72 Activates the Notch1/c-Myc Pathway to Promote Stemness Transformation of Ovarian Cancer Cells. Front. Cell Dev. Biol. 2020, 8, 583087. [Google Scholar] [CrossRef]
  45. Christel, C.; Lee, A. Ca2+-Dependent Modulation of Voltage-Gated Ca2+ Channels. Biochim. Biophys. Acta 2012, 1820, 1243–1252. [Google Scholar] [CrossRef] [PubMed]
  46. Li, G.; Petkova, T.D.; Laritsky, E.; Kessler, N.; Baker, M.S.; Zhu, S.; Waterland, R.A. Early Postnatal Overnutrition Accelerates Aging-Associated Epigenetic Drift in Pancreatic Islets. Environ. Epigenet. 2019, 5, dvz015. [Google Scholar] [CrossRef] [PubMed]
  47. Vendel, A.C.; Rithner, C.D.; Lyons, B.A.; Horne, W.A. Solution Structure of the N-Terminal A Domain of the Human Voltage-Gated Ca2+ channel Beta4a Subunit. Protein Sci. 2006, 15, 378–383. [Google Scholar] [CrossRef] [PubMed]
  48. Buraei, Z.; Yang, J. The ß Subunit of Voltage-Gated Ca2+ Channels. Physiol. Rev. 2010, 90, 1461–1506. [Google Scholar] [CrossRef]
  49. Cork, R.J.; Namkung, Y.; Shin, H.-S.; Mize, R.R. Development of the Visual Pathway Is Disrupted in Mice with a Targeted Disruption of the Calcium Channel Beta(3)-Subunit Gene. J. Comp. Neurol. 2001, 440, 177–191. [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. 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]
  52. Qin, Y.; Sun, W.; Wang, Z.; Dong, W.; He, L.; Zhang, T.; Zhang, H. Long Non-Coding Small Nucleolar RNA Host Genes (SNHGs) in Endocrine-Related Cancers. OTT 2020, 13, 7699–7717. [Google Scholar] [CrossRef]
  53. Mostmans, Y.; Cutolo, M.; Giddelo, C.; Decuman, S.; Melsens, K.; Declercq, H.; Vandecasteele, E.; De Keyser, F.; Distler, O.; Gutermuth, J.; et al. The Role of Endothelial Cells in the Vasculopathy of Systemic Sclerosis: A Systematic Review. Autoimmun. Rev. 2017, 16, 774–786. [Google Scholar] [CrossRef]
  54. Errasti-Murugarren, E.; Bartoccioni, P.; Palacín, M. Membrane Protein Stabilization Strategies for Structural and Functional Studies. Membranes 2021, 11, 155. [Google Scholar] [CrossRef]
  55. Zenkel, M.; Schlotzer-Schrehardt, U.; Berner, D.; Kruse, F.E.; Reis, A.; Khor, C.C.; Aung, T.; Pasutto, F. Dysregulated Expression of POMP and TMEM136 May Contribute to Impaired Proteasome Function and Endothelial Dysfunction in Eyes with Pseudoexfoliation Syndrome/Glaucoma. Investig. Ophthalmol. Vis. Sci. 2017, 58, 4902. [Google Scholar]
  56. Pinelli, M.; Carissimo, A.; Cutillo, L.; Lai, C.-H.; Mutarelli, M.; Moretti, M.N.; Singh, M.V.; Karali, M.; Carrella, D.; Pizzo, M.; et al. An Atlas of Gene Expression and Gene Co-Regulation in the Human Retina. Nucleic Acids Res. 2016, 44, 5773–5784. [Google Scholar] [CrossRef] [PubMed]
  57. Orphal, M.; Gillespie, A.; Böhme, K.; Subrova, J.; Eisenreich, A.; Kreutz, R. TMEM63C, a Potential Novel Target for Albuminuria Development, Is Regulated by MicroRNA-564 and Transforming Growth Factor Beta in Human Renal Cells. Kidney Blood Press. Res. 2020, 45, 850–862. [Google Scholar] [CrossRef] [PubMed]
  58. Du, H.; Ye, C.; Wu, D.; Zang, Y.-Y.; Zhang, L.; Chen, C.; He, X.-Y.; Yang, J.-J.; Hu, P.; Xu, Z.; et al. The Cation Channel TMEM63B Is an Osmosensor Required for Hearing. Cell Rep. 2020, 31, 107596. [Google Scholar] [CrossRef] [PubMed]
  59. Shah, V.N.; Chagot, B.; Chazin, W.J. Calcium-Dependent Regulation of Ion Channels. Calcium Bind. Proteins 2006, 1, 203–212. [Google Scholar]
  60. Ma, L.; Liu, X.; Liu, Q.; Jin, S.; Chang, H.; Liu, H. The Roles of Transient Receptor Potential Ion Channels in Pathologies of Glaucoma. Front. Physiol. 2022, 13, 806786. [Google Scholar] [CrossRef]
  61. Jiang, Y.; Chiu, C.-Y.; Yan, Q.; Chen, W.; Gorin, M.B.; Conley, Y.P.; Lakhal-Chaieb, M.L.; Cook, R.J.; Amos, C.I.; Wilson, A.F.; et al. Gene-Based Association Testing of Dichotomous Traits with Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration. J. Am. Stat. Assoc. 2021, 116, 531–545. [Google Scholar] [CrossRef]
  62. Schulz, A.; Müller, N.V.; van de Lest, N.A.; Eisenreich, A.; Schmidbauer, M.; Barysenka, A.; Purfürst, B.; Sporbert, A.; Lorenzen, T.; Meyer, A.M.; et al. Analysis of the Genomic Architecture of a Complex Trait Locus in Hypertensive Rat Models Links Tmem63c to Kidney Damage. Elife 2019, 8, e42068. [Google Scholar] [CrossRef]
  63. Lim, B.J.; Yang, J.W.; Do, W.S.; Fogo, A.B. Pathogenesis of Focal Segmental Glomerulosclerosis. J. Pathol. Transl. Med. 2016, 50, 405–410. [Google Scholar] [CrossRef]
  64. Villarreal, G.; Oh, D.-J.; Kang, M.H.; Rhee, D.J. Coordinated Regulation of Extracellular Matrix Synthesis by the MicroRNA-29 Family in the Trabecular Meshwork. Investig. Ophthalmol. Vis. Sci. 2011, 52, 3391. [Google Scholar] [CrossRef]
  65. Li, H.; Raghunathan, V.; Stamer, W.D.; Ganapathy, P.S.; Herberg, S. Extracellular Matrix Stiffness and TGFβ2 Regulate YAP/TAZ Activity in Human Trabecular Meshwork Cells. Front. Cell Dev. Biol. 2022, 10, 844342. [Google Scholar] [CrossRef] [PubMed]
  66. Fuchshofer, R.; Stephan, D.A.; Russell, P.; Tamm, E.R. Gene Expression Profiling of TGFbeta2- and/or BMP7-Treated Trabecular Meshwork Cells: Identification of Smad7 as a Critical Inhibitor of TGF-Beta2 Signaling. Exp. Eye Res. 2009, 88, 1020–1032. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Aydın Yaz, Y.; Yıldırım, N.; Yaz, Y.; Tekin, N.; İnal, M.; Şahin, F.M. Role of Oxidative Stress in Pseudoexfoliation Syndrome and Pseudoexfoliation Glaucoma. Turk. J. Ophthalmol. 2019, 49, 61–67. [Google Scholar] [CrossRef] [PubMed]
  68. Pattabiraman, P.P.; Maddala, R.; Rao, P.V. Regulation of Plasticity and Fibrogenic Activity of Trabecular Meshwork Cells by Rho GTPase Signaling. J. Cell. Physiol. 2014, 229, 927–942. [Google Scholar] [CrossRef]
  69. Tanna, A.P.; Johnson, M. Rho Kinase Inhibitors as a Novel Treatment for Glaucoma and Ocular Hypertension. Ophthalmology 2018, 125, 1741–1756. [Google Scholar] [CrossRef]
  70. Filla, M.S.; Faralli, J.A.; Peotter, J.L.; Peters, D.M. The Role of Integrins in Glaucoma. Exp. Eye Res. 2017, 158, 124–136. [Google Scholar] [CrossRef]
  71. Tezel, G.; Wax, M.B. Increased Production of Tumor Necrosis Factor-Alpha by Glial Cells Exposed to Simulated Ischemia or Elevated Hydrostatic Pressure Induces Apoptosis in Cocultured Retinal Ganglion Cells. J. Neurosci. 2000, 20, 8693–8700. [Google Scholar] [CrossRef]
Cells 11 02738 g001 550
Figure 1. Lower snoRNA expression in the PEXG group compared to the cataract group. Data are shown as mean [log2].
Figure 1. Lower snoRNA expression in the PEXG group compared to the cataract group. Data are shown as mean [log2].
Cells 11 02738 g001
Cells 11 02738 g002 550
Figure 2. Higher snoRNA expression in the PEXG group compared to the cataract group. Data are shown as mean [log2].
Figure 2. Higher snoRNA expression in the PEXG group compared to the cataract group. Data are shown as mean [log2].
Cells 11 02738 g002
Cells 11 02738 g003 550
Figure 3. Cluster analysis results based on the expression levels of the detected snoRNAs.
Figure 3. Cluster analysis results based on the expression levels of the detected snoRNAs.
Cells 11 02738 g003
Cells 11 02738 g004 550
Figure 4. Terms of the Gene Ontology (GO): Biological Processing (GO:BP), GO: Cellular Compartment (GO:CC), GO: Molecular Function (GO:MF), and REACTOME categories, revealed for glaucoma- and/or pseudoexfoliation syndrome (PEX)-associated genes targeted by snoRNAs 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 R.
Figure 4. Terms of the Gene Ontology (GO): Biological Processing (GO:BP), GO: Cellular Compartment (GO:CC), GO: Molecular Function (GO:MF), and REACTOME categories, revealed for glaucoma- and/or pseudoexfoliation syndrome (PEX)-associated genes targeted by snoRNAs 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 R.
Cells 11 02738 g004
Table
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
Table 2. List of the snoRNAs examined in the study and interactions with genes and snoRNAs.
Table 2. List of the snoRNAs examined in the study and interactions with genes and snoRNAs.
SymbolBox TypeHost GenesTarget GenesTarget snoRNAs
SNORD73BC/DRPS3A--
SNORD58AC/DRPL17--
SNORD56C/DEDEM2--
LINC02463--
AC012501.2--
ITGB8--
NOP56-SNORA7B
SGSM1-SNORA65, SNORD50A
-FAM120AOS-
SNORA77H/ACARERE--
ATP2B4 OR ATP2B2
SNORA72H/ACARPL30--
NUCKS1--
ECT2--
DEGS1--
SNORA64H/ACAMYRIP--
RPS2RPS2, TNPO2-
PLS3-AS1--
-CACNB3-
SNORA32H/ACATAF1D--
TMEM63C--
snoRNAs—small nucleolar RNAs.
Table
Table 3. Features of genes regulated by snoRNAs examined in the study.
Table 3. Features of genes regulated by snoRNAs examined in the study.
SymbolChromosomeDescription
CACNB312q13.12Calcium voltage-gated channel auxiliary subunit beta 3
FAM120AOS9q22.31Family with sequence similarity 120A opposite strand
RPS216p13.3Ribosomal protein S2
TNPO219p13.13Transportin 2
Table
Table 4. Lower snoRNAs’ expression in the PEXG group compared to the cataract group [log2].
Table 4. Lower snoRNAs’ expression in the PEXG group compared to the cataract group [log2].
snoRNAGroupNMeanSDFold Changep-ValueAUCAUC p-Value
SNORA32Cataract91.4310.158−1.140.00720.8830.0062
PEXG91.1960.166
SNORA72Cataract91.2300.169−1.150.00470.8700.0081
PEXG91.0120.106
SNORD56Cataract91.3620.202−1.210.00220.9010.0041
PEXG91.0490.161
SNORA64Cataract91.1490.090−1.100.00450.8700.0081
PEXG90.9980.104
SNORD73bCataract91.3970.163−1.120.00480.9070.0036
PEXG91.1810.112
AUC—area under the curve.
Table
Table 5. Higher snoRNA expression in the PEXG group compared to the cataract group [log2].
Table 5. Higher snoRNA expression in the PEXG group compared to the cataract group [log2].
snoRNAGroupNMeanSDFold Changep-ValueAUCAUC p-Value
SNORD58ACataract91.2110.1771.130.00860.8150.0243
PEXG91.4040.079
SNORA77Cataract91.0630.0731.210.00030.9440.0015
PEXG91.3360.159
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Gasińska, K.; Czop, M.; Kosior-Jarecka, E.; Wróbel-Dudzińska, D.; Kocki, J.; Żarnowski, T. Small Nucleolar RNAs in Pseudoexfoliation Glaucoma. Cells 2022, 11, 2738. https://doi.org/10.3390/cells11172738

AMA Style

Gasińska K, Czop M, Kosior-Jarecka E, Wróbel-Dudzińska D, Kocki J, Żarnowski T. Small Nucleolar RNAs in Pseudoexfoliation Glaucoma. Cells. 2022; 11(17):2738. https://doi.org/10.3390/cells11172738

Chicago/Turabian Style

Gasińska, Karolina, Marcin Czop, Ewa Kosior-Jarecka, Dominika Wróbel-Dudzińska, Janusz Kocki, and Tomasz Żarnowski. 2022. "Small Nucleolar RNAs in Pseudoexfoliation Glaucoma" Cells 11, no. 17: 2738. https://doi.org/10.3390/cells11172738

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop