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Review

Shared Molecular Pathways in Glaucoma and Other Neurodegenerative Diseases: Insights from RNA-Seq Analysis and miRNA Regulation for Promising Therapeutic Avenues

by
Carlos Franciney Moreira Vasconcelos
1,2,*,
Vinicius Toledo Ribas
3 and
Hilda Petrs-Silva
2,*
1
University of Medicine of Göttingen, 37075 Göttingen, Germany
2
Institute of Biophysics Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil
3
Institute of Biological Sciences, Universidade Federal de Minas Gerais (ICB/UFMG), Belo Horizonte 31270-901, Brazil
*
Authors to whom correspondence should be addressed.
Cells 2023, 12(17), 2155; https://doi.org/10.3390/cells12172155
Submission received: 29 July 2023 / Revised: 14 August 2023 / Accepted: 21 August 2023 / Published: 28 August 2023

Abstract

:
Advances in RNA-sequencing technologies have led to the identification of molecular biomarkers for several diseases, including neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, Huntington’s diseases and Amyotrophic Lateral Sclerosis. Despite the nature of glaucoma as a neurodegenerative disorder with several similarities with the other above-mentioned diseases, transcriptional data about this disease are still scarce. microRNAs are small molecules (~17–25 nucleotides) that have been found to be specifically expressed in the CNS as major components of the system regulating the development signatures of neurodegenerative diseases and the homeostasis of the brain. In this review, we sought to identify similarities between the functional mechanisms and the activated pathways of the most common neurodegenerative diseases, as well as to discuss how those mechanisms are regulated by miRNAs, using RNA-Seq as an approach to compare them. We also discuss therapeutically suitable applications for these disease hallmarks in clinical future studies.

1. Introduction

Neurodegenerative diseases, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD) and Amyotrophic Lateral Sclerosis (ALS) are characterized by a progressive loss of specific neuronal cell populations, in addition to other features such as abnormal protein aggregation, mitochondrial dysfunction, pro-oxidative and pro-inflammatory mechanisms [1,2,3]. Because of the neuronal cell loss, individuals affected by these types of pathology may present several neurological deficits, including cognitive, motor and behavioral dysfunctions, which impair their quality of life and ability to perform tasks [4,5]. AD and PD are the two most common neurodegenerative disorders in the elderly, which have been associated with comorbidities such as neuropsychiatric dysfunctions [6], ischemic episodes [7] and eye diseases, including Open-Angle Glaucoma (OAG) [8,9].
OAG is one of the most common eye diseases whose frequency increases with age, leading to vision impairment and, frequently, irreversible blindness [7,8,9,10]. Epidemiological predictions estimate that OAG will affect over 95.4 million individuals by 2030 [11]. The progressive visual loss and functional working effects, along with injuries affecting patient quality of life, have been reported as one of the most important public health problems, especially among elderly people [10,12]. Furthermore, OAG is characterized by the neurodegeneration of the retinal ganglion cells (RGCs), which are projection neurons located in the inner part of the retina that project their axons via the optic nerve to the superior responsive regions on the central nervous system (CNS), projecting a visual stimulus from the eye to the brain [10]. This neurodegenerative process leads to the stimulation of inflammatory and pro-oxidative mechanisms, as well as to mitochondrial dysfunction and progressive neuronal death, similar to the above-mentioned neurodegenerative diseases [8,13,14].
Currently, advances in RNA-sequencing (RNA-Seq) technologies have led to the identification of several molecular biomarkers, as well as to the accurate observation of global transcriptional alterations that are associated with the hallmarks of several diseases, including neurodegenerative ones [15]. Moreover, recent works have increased the identification of microRNAs (miRNAs) as a potential diagnostic biomarker in brain diseases [16,17]. miRNAs are small noncoding RNA molecules responsible for regulating protein homeostasis through binding to a targeted messenger RNA (mRNA), causing its cleavage, translational inhibition or degradation [15,17]. In fact, several miRNAs have been found to be specifically expressed in the CNS and involved in the regulation of the development signatures for neurodegenerative diseases, such as AD [15,18,19,20], PD [21], HD [22] and Creutzfeldt–Jacob disease (sCJD) [15], but data regarding transcriptional changes and miRNA regulation in glaucoma are still scarce.
In this review, we aimed to identify the similarities in functional mechanisms and activated pathways between the most common neurogenerative diseases, discuss how those mechanisms are regulated by miRNAs, using RNA-Seq as an approach to compare them, as well as highlight possible new therapeutical avenues for the application of miRNA-based drugs in future pre-clinical studies and clinical trials.

2. Glaucoma Similarity Hallmarks with Other Neurodegenerative Diseases

The retina is an extension of the brain having several similarities with it, such as specialized immune responses with local arrays of cytokines and surface molecules [23]. This is also exemplified in multiple well-defined neurodegenerative processes that happens in the brain and have manifestations in the retina (Figure 1). Additionally, ocular disfunctions frequently precede the diagnosis of CNS disorders [23,24]. Moreover, the retina can be analyzed by less invasive optical techniques than those used in brain examination, such as optical coherence tomography (OCT), and reflects pathological features in the brain, providing a window for an earlier diagnosis of CNS disorders, including neurodegenerative diseases [24,25].
In OAG patients, which is the most common form of glaucoma, there is a progressive increase in intraocular pressure (IOP) due to an impaired aqueous humor outflow [26]. However, recent research has shown that the sensitivity of the optic nerve to IOP is more relevant for glaucoma progression than the IOP itself [27]. In fact, some patients with increased IOP not always present neurodegeneration [26,28]. Additionally, the optic nerve head (ONH) is vulnerable to injury due to its over 1.5 million RGC unmyelinated axons, which can provide a unique environment for neurodegenerative processes in Glaucoma. There is a complex interplay between neuronal, glial, biochemical and vascular components in the ONH that can be influenced by alterations in the IOP [26,29,30]. These mechanisms can be impaired by aging but also by exacerbated inflammatory responses, such as astroglia activation and proliferation, increased deposition of collagen and changes in the extracellular matrix, which are not dependent on genetic inheritance or predisposition [26]. Moreover, it has been widely reported that immune-mediated events can trigger and contribute to the progression of several neurodegenerative diseases, including AD, PD, HD, ALS, among others [1,2,24,26].
The neuroinflammatory response is considered to be crucial for both regenerative and neurodegenerative mechanisms in the CNS, including the retina, and immunocompetent cells are key factors in the appearance of morphological changes, cell migration and proliferation related to these injuries [31,32]. Several studies demonstrated changes in the nerve fiber layer of the retina in patients with AD and PD, due to microvascular and structural alterations, as well as reduced blood flow associated with retinal thinning [24,33,34]. In patients with AD, neuroinflammation is a crucial hallmark of the pathology. The elevation in pro-inflammatory cytokines, due to a reactive astroglia response, leads to an accumulation of Aβ oligomers and tau protein plaques, resulting in neuronal loss [23,24]. Interestingly, the APP protein (precursor of Aβ oligomers) was found to be one of the most abundant proteins in the optic nerve and is transferred to the axon plasma membrane in small vesicles [23]. The elevation of the IOP has been reported to induce Aβ aggregate formation in RGCs in an experimental rat model of glaucoma [35]. Additionally, hyperphosphorylated tau protein was also detected in the retinas of glaucoma patients [7]. Similarly, PD patients demonstrated α-synuclein accumulation also in the retina, which underlies very early visual impairment symptoms [36]. In addition to the activation of the immune response, there is also evidence of transient optic disc microhemorrhages in patients regardless of the increased IOP levels, indicating an impairment of the blood–retina barrier (BRB) and the infiltration of circulating immune cells, which are associated with the progression of the disease [37,38,39]. This infiltration due the BRB rupture may represent some of the autoimmune components observed in humans and animal models [40]. Interestingly, the increased IOP can also cause an initial influx of neutrophils and macrophages that express, for example, oncomudulin and SDF1, stimulating axon outgrowth, showing a pro-regenerative mechanism under injury stress [41,42,43].
In the retina and optic nerve, the immune cells (e.g., microglia, astrocytes and Müller glia) act as a surveillance system, maintaining the homeostasis by releasing neuroprotective and growth factors, as well as in the clearance of cell debris [26]. Polyunsaturated fatty acids (PUFAs) are an important component of the cell membrane in the brain and are crucial for gene transcription, cell signaling and caspase activation [44,45]. RGC axons are myelinated distal to the lamina cribrosa. Cell culture studies showed that omega-3 PUFA can stimulate myelin production, and it is possible that a lack of omega-3 can lead to an alteration of myelin composition and to RGC synaptic disfunction [46]. PUFAs have a role in the treatment of PD [47], which shows, therefore, a correlation between the management of glaucoma and that of parkinsonism. Additionally, optic neuritis, which can be an early sign of multiple sclerosis (MS), is an autoimmune demyelinating and neurodegenerative disease. Its pathogenic mechanisms also include inflammation involving T- and B-lymphocytes and circulating innate immunity cells, as well as oxidative stress [48]. Patients with MS also show a reduction in the thickness of the retinal nerve fiber layer, as well as reduced optic nerve perfusion. OCT measures were able to detect RGC loss and axonal damage due to MS [48,49].
In humans, the inheritance of the APOE4 allele increases the chance of developing AD [50]. Moreover, it was also reported that mouse models expressing APOE4 demonstrated a higher microglia and astrocyte reactivity response, suggesting that even in the absence of AD, APOE4 pro-inflammatory induction, as well as its correlation with dysfunctional microglial clearance of Aβ, may trigger neurodegeneration [50,51,52]. Additionally, APOE4 showed to be protective against glaucoma and age-related macular disease (AMD), indicating, perhaps, that less reactive microglia are protective in the retina, whereas dysfunctional microglia are detrimental in AD [26,53]. The association of APOE with high-density lipoprotein (HDL) level was also reported as a neuroprotective target for clearance of α-synuclein in PD [54].
Other main hallmarks of neurodegenerative disorders are oxidative stress and mitochondrial dysfunction. The latter was reported in PD by the mediation of mitophagy and autosomal recessive phenotype induced by mutations in the PTEN-induced putative kinase 1 (PINK1) and PRKN (PARK2) genes [55]. PINK1 and LRRK2 are enriched in astrocytes, and their mutations can induce α-synuclein aggregation on the mitochondrial outer membrane [56,57]. In AD, on the other hand, Aβ aggregates cause an increase in the production of reactive oxygen species (ROS), affecting mitochondrial axonal transport, energy metabolism, mitophagy and mitochondrial fusion/fission [58]. Oxidative stress and mitochondrial dysfunction have also been associated with RGC degeneration, both in animal models and in humans [14,59]. In fact, aged mice have shown alterations in mitochondrial transport in RGCs and are more susceptible to an increased IOP than young mice [60]. The elevation in IOP have also been reported to affect mitochondrial bioenergetics in the brain’s visual cortex in rats, as well as lead to reduced ATP production, increased superoxide production and differences in the mitochondrial complex activity [61]. Moreover, RGCs and optic nerve fibers are rich in mitochondria, and the reduction in energy metabolism along with increased ROS production at the mitochondrial level are potential mechanisms to be considered in the etiopathogenesis of glaucoma [24]. Thus, the identification of cellular and molecular mechanisms related to RGC death is a major important step towards the discovery of new biomarkers and the development of therapeutic strategies against glaucoma.

3. RNA-Seq Analysis as a Tool to Search for Similar Pathway Regulation Mechanisms in Neurodegenerative Diseases

During the last decade, advances in RNA-Seq technologies have demonstrated the great potential of this approach to identify changes in gene networks and cellular pathways involved in various aspects of different diseases in a broad way [62]. Transcriptomic studies have provided analyses of expression variations of genes, as well as the quantification of their transcriptional level under different experimental conditions or in patients [63,64]. Moreover, access to these big data sets allows the interpretation and design of more effective functional analyses of candidate gene biomarkers, as well as of other transcripts [65], thus, providing an important tool for prospective investigations and future experimental validations.
With the emergence of Genome-Wide Association Studies (GWAS), researchers have been able to determine similarities between pathways and gene interaction networks across the entire genome in large cohorts, facilitating the identification of genetic variations associated with several neurodegenerative diseases. GWAS rely on significant top loci racks, and the assumption of genetic association similarities between diseases is made to identify overlaps; however, this assumption may overlook the fact that a strong genetic locus in one disease may not have a similar or significant impact in another disease [66]. Then, further investigations are necessary to discern each discovery’s unique genetic intricacies and avoid oversimplification in unraveling the complex genetic landscape that contributes to different conditions.
Previous work using RNA-Seq and gene expression profile meta-analyses showed interesting gene signature comparisons between neurodegenerative diseases. The analysis of different areas in 1270 post-mortem brain tissues from 13 patient cohorts affected by four diseases (AD, PD, HD and ALS) identified 243 examples of shared gene expression between the diseases, which were validated with another dataset comprising 205 samples from 15 cohorts [67]. Those shared genes were commonly reported in the literature of neurodegenerative altered pathways, including neuroinflammation, mitochondrial dysfunction, oxidative stress and synaptic plasticity impairments [66,68].
Although all neurodegenerative diseases have significant genetic and inherited components, whose contribution in AD, PD and ALS is estimated to be 60–80% [69,70], ~40% [71], and ~60% [72], respectively, most cases exhibit a complex etiology, involving sporadic elements [66]. Genetically based, inherited cases of these diseases have been attributed to rare mutations, such as those in the amyloid precursor protein (APP) and presenilin genes (PSEN1, PSEN2) for AD, α-synuclein (SNCA), PRKN (PARK2), PINK1, microtubule-associated protein tau (MAPT) and leucine-rich repeat kinase 2 or dardarin (LRRK2) for PD, cytosolic Cu/Zn superoxide dismutase (SOD1), alsin (ALS2), senataxin (SETX), TAR DNA-binding protein 43 (TARDBP) and synaptobrevin/VAMP (vesicle-associated membrane protein)-associated protein B (VAPB) for ALS [68,72,73]. Regarding glaucoma, although some elements of monogenic and polygenic Mendelian inheritance have been presented in several cases, common mutations in genes, such as MYOC and OPTN, or copy number variants in TBK1, only account for less than 5% of the genetic patterns of the disease [74]. Nonetheless, comparisons between public RNA-Seq data sets, also from other types of CNS neurodegeneration, identified common pathway alterations between different models of optical nerve damage, such as the DBA/2J mouse model, optic nerve crush, axotomy models [75]. These studies are in accordance with the enrichment of neuroinflammatory, pro-oxidative, and immune responses found for other neurodegenerative disorders. Furthermore, the expansion of these big data analyses using protein–protein interaction networks, KEGG pathways and gene ontology databases, could help researchers to find similar biomarkers that can further be used in pre-clinical and clinical studies. As an example, Li and collaborators [76] expanded a set of 10 common disease susceptibility genes shared between AD, HD and PD (e.g., ESR2, PARP1, GSK3B, UCHL1 and LRRK2) into a larger set of 1294 genes connected to the first set of genes in a protein network analysis. Furthermore, similar to mRNA expression profile analysis, several studies found that many important responsive mechanisms in neurodegenerative disorders are regulated by miRNAs. The up- and down-regulation of these molecules’ expression was described in several altered pathways in many neurodegenerative and brain disorders. Thus, their investigation could lead to the discovery of even more relevant biomarker candidates than mRNAs, due to their stability in many human biofluids and potential dysregulation in earlier stages of the diseases [68,77]. Adding that fact to the above-discussed sporadic and non-genetic character of many cases of neurodegenerative disorders, those molecules could bring new insights for future studies regarding the regulation of common mechanisms in brain homeostasis maintenance during neurogenerative injuries.

4. miRNAs as Potential Therapeutic Agents in Neurodegenerative Disorders

Several miRNAs were found to be expressed specifically in the CNS and to be dysregulated in neurodegenerative diseases [18,19,20,21,22]. These dysregulated miRNAs can target key genes involved in disease-associated pathways, thereby influencing disease progression. For example, miR-29a was shown to regulate genes involved in extracellular matrix remodeling, which is implicated in the pathogenesis of AD and PD [19,21]. miR-132 and miR-212 were found to be downregulated in AD and HD, and their targets include genes involved in synaptic plasticity and neuroprotection [18,22]. miR-124, which is highly expressed in the brain, was shown to regulate neuronal differentiation and is dysregulated in various neurodegenerative diseases [15]. These examples highlight the importance of miRNAs in modulating disease-associated pathways and provide potential targets for therapeutic interventions.
However, given the similarities in the functional mechanisms and activated pathways between glaucoma and other neurodegenerative diseases, it is plausible to hypothesize that miRNAs may also play a role in glaucoma pathogenesis. RNA-Seq analysis can be a valuable tool to identify differentially expressed miRNAs in glaucoma and explore their potential involvement in disease-related pathways. By comparing the transcriptomic profiles of glaucoma samples with those of other samples of neurodegenerative diseases, common dysregulated miRNAs and pathways can be identified, providing insights into the shared molecular mechanisms underlying these diseases. Furthermore, identifying miRNAs as potential diagnostic biomarkers in neurodegenerative diseases opens the possibility of developing miRNA-based therapeutics. Modulating the expression or activity of disease-associated miRNAs may offer a novel approach to the treatment of glaucoma and other neurodegenerative diseases. Strategies such as miRNA replacement therapy or miRNA inhibition using antisense oligonucleotides (ASOs) can be explored.
The biogenesis of miRNAs involves a series of steps. It begins in the cell nucleus, where miRNAs are transcribed from specific genomic loci by RNA polymerase II or, in some cases, RNA polymerase III [78,79]. These primary transcripts, called pri-miRNAs, can be several hundred to thousands of nucleotides long and often contain a stem–loop structure. The next step involves the processing of pri-miRNAs by an enzyme complex known as the microprocessor complex, which consists of the RNase III enzyme Drosha and its co-factor DGCR8 (DiGeorge syndrome critical region 8) in humans [78,79]. The microprocessor complex cleaves the pri-miRNA into a smaller hairpin-shaped RNA molecule known as the precursor miRNA (pre-miRNA). The pre-miRNA is then exported from the nucleus to the cytoplasm by the nuclear export protein Exportin-5 [78,79]. In the cytoplasm, the pre-miRNA is further processed by another RNase III enzyme called Dicer, along with its co-factor TRBP (TAR RNA-binding protein) or PACT (protein activator of the interferon-induced protein kinase) in humans [78,79]. Dicer cleaves the pre-miRNA, resulting in the formation of a small RNA duplex. The RNA duplex consists of two strands: the mature miRNA (guide strand) and the miRNA* (star strand). The miRNA* strand is typically degraded, while the mature miRNA strand is loaded into a multi-protein complex known as the RNA-induced silencing complex (RISC) [78,79]. Within the RISC complex, the mature miRNA guides the complex to target mRNAs with complementary sequences in their 3′ UTRs.
The mechanism of action of miRNAs involves base-pairing interactions between the miRNA and its target mRNA. The miRNA recognizes its target mRNA through partial complementarity between the miRNA sequence and the target mRNA sequence. The binding of the miRNA to the mRNA can lead to translational repression or mRNA degradation, depending on the degree of complementarity and other factors. In the case of translational repression, the binding of the miRNA to the mRNA blocks the translation process, preventing the synthesis of the corresponding protein [78,79]. Overall, miRNAs play a critical role in regulating gene expression by modulating the stability and translation of target mRNAs. They are involved in a wide range of biological processes, including development, cell differentiation, proliferation, and response to environmental cues. The dysregulation of miRNAs has been implicated in various diseases, such as cancer, neurodegenerative disorders and cardiovascular diseases, highlighting their significance in both normal physiology and pathology. In fact, several potential therapeutic miRNAs have been identified as promising targets for the treatment of different neurodegenerative disorders. Here are some examples.
1. Alzheimer’s disease (AD): AD is characterized by the accumulation of amyloid-beta (Aβ) plaques and neurofibrillary tangles in the brain. miRNAs such as miR-124, miR-29a and miR-34a have been implicated in AD pathogenesis. These miRNAs regulate the expression of genes involved in Aβ production, tau phosphorylation and neuroinflammation [80]. miR-124 and miR-29a/b regulate the Aβ synthesis pathway by targeting the 3′ UTR of the BACE1 mRNA precursor, promoting the regulation of the APP expression [81,82]. miR-124 increased the number of apoptotic and necrotic cells in vitro in a PC12 cellular AD model and participated in the regulation of BACE1 expression [81]. miR-34a regulated Tau expression in vitro, both in neuroblastoma and in HEK 293 cell models [83,84] and, additionally, enhanced NMDA and AMPA receptors expression in the APP/PS1 mice model [84]. Modulating the levels of these miRNAs could potentially mitigate AD-related pathology.
2. Parkinson’s disease (PD): PD is characterized by the loss of dopaminergic neurons in the substantia nigra of the brain and α-synuclein neurofilaments’ accumulation. miRNAs such as miR-134a, miR-133b, miR-7, miR-153 and miR-34b/c were found to play a role in PD pathogenesis. These miRNAs regulate the expression of genes involved in neuronal survival, dopamine synthesis and mitochondrial function [85,86]. The reduction of miR-133b enhanced the overexpression of SNCA, which induced the production of α-synuclein, leading to a major sensitivity of dopaminergic neurons to oxidative stress [87]. The activation of PI3K/Akt signaling can also induce α-synuclein overexpression, while the activity of miR-7 and miR-153 can inhibit the PI3K/Akt signaling pathway. Moreover, the PI3K/Akt, LRRK2/ERK1/2 and PI3K/PTEN signaling pathways are involved in neuronal apoptosis, oxidative stress and neuroinflammation. miR-34a and miR-153 were associated with the regulation of those mechanisms, reducing the progression the disease [86,88]. Thus, modulating these miRNAs may have therapeutic potential in PD.
3. Huntington’s disease (HD): HD is caused by a mutation in the huntingtin gene, resulting in the accumulation of a toxic mutant huntingtin protein in neurons. miRNAs such as miR-9, miR-124, miR-140 and miR-34a were identified as potential therapeutic targets for HD. These miRNAs modulate the expression of genes involved in neuronal survival, mutant huntingtin protein clearance and neuroinflammation. miR-9 increased the expression of RE1 in leukocytes and was downregulated in peripheral leukocytes of HD patients [89]. miR-140 regulated disintegrin and metalloproteinase 10 (ADAM 10) expression, which reduced the excessive cleavage of the synaptic protein N-cadherin at the postsynaptic densities, impairing synaptic transmission [90]. miR-124 is a crucial regulator of neuronal differentiation in neurodegenerative processes. The injection of miR-124 increased the expression of the neuroprotective proteins PGC-1α and BDNF in a mice model and downregulated the SRY-related HMG box transcriptional factor 9, a repressor of cell differentiation [91]. Modulating the levels of these miRNAs could potentially attenuate HD pathology.
4. Amyotrophic lateral sclerosis (ALS): ALS is characterized by the progressive degeneration of motor neurons in the spinal cord and brainstem. miRNAs such as miR-34a, miR-183, miR-218 and miR-335 have been implicated in ALS pathogenesis. These miRNAs regulate the expression of genes involved in motor neuron development, survival and neuroinflammation. For instance, miR-34a acts by targeting the 3′UTR of the XIAP mRNA precursor, promoting protective effects against oxidative stress-induced apoptosis through SIRT1 [5,92]. miR-335, miR-183 and miR-218 expression is downregulated in ALS patient’s serum and in mouse models; this affects several disease-linked mechanisms, such as the regulation of caspase 7 expression [93]. Modulating the levels of these miRNAs could potentially modulate the immune response and reduce oxidative stress and apoptosis in ALS.
5. Open-angle Glaucoma (OAG): OAG is characterized by an increase in the IOP and a consequent progressive degeneration of RGCs, causing blindness in most cases. miRNA such as miR-204, miR-124, miR-125a and miR-125b have been implicated in OAG pathogenesis. Among the large number of miRNAs involved in the pathophysiology of the retina, miR-204 appears to be one of the major players in retinal development and disease [94]. Additionally, the involvement of miR-124, miR-125a, miR-125b and miR-204 in the development of retinal cells in adult mice has already been confirmed [95]. miR-125b was shown to play indispensable functions during the differentiation and maintenance of retinal pigment epithelium (RPE) cells [96]. miR-29 was demonstrated to be a negative modulator of the expression of collagens and other key components of the extracellular matrix in human TM cells and to decrease cytotoxicity in the presence of chronic oxidative stress [97].
It is important to note that while the modulation of miRNAs shows promise as a therapeutic strategy for neurodegenerative diseases (Table 1), further research is needed to fully understand the roles and potential clinical applications of miRNAs. Additionally, developing safe and efficient delivery methods for miRNA-based therapies remains a challenge in the field. Nonetheless, targeting specific miRNAs holds significant potential for the development of novel treatments for several diseases. While neurodegenerative diseases are distinct disorders with unique pathological mechanisms, there is some overlap in the miRNAs that have shown therapeutic effects across different neurodegenerative diseases. Here are a few examples of miRNAs that demonstrated potential therapeutic benefits in multiple neurodegenerative diseases:
1. miR-124: miR-124 is one of the most abundant miRNAs in the brain and is involved in regulating neuronal development and function. It was shown to have therapeutic effects in AD, PD, HD and OAG. In AD, miR-124 was found to target genes involved in Aβ production and tau hyperphosphorylation [81,94]. In PD, it was shown to protect dopaminergic neurons and regulate inflammation [98,99]. In HD, miR-124 was reported to modulate the expression of genes associated with mutant huntingtin protein clearance and neuroinflammation [91,100]. In OAG, miR-124 was reported as a key regulator of the development and maintenance of retinal cells in adult mice [96].
2. miR-9: miR-9 is involved in neuronal development and has been implicated in several neurodegenerative diseases, including AD, PD and HD. In AD, miR-9 was found to target genes associated with Aβ production and neuroinflammation [101]. In PD, it was shown to regulate genes involved in dopaminergic neuron survival and mitochondrial function [16,98]. In HD, miR-9 was reported to modulate genes related to mutant huntingtin protein clearance and neuroinflammation [99].
3. miR-146a: miR-146a is known for its role in regulating immune responses and inflammation. It has been implicated in AD, PD and multiple sclerosis (MS). In AD, miR-146a was shown to regulate genes involved in neuroinflammation and Aβ clearance [102]. In PD, it was found to modulate inflammation and dopaminergic neuron survival [103]. In MS, miR-146a was reported to regulate immune cell activation and the inflammatory response [104].
4. miR-155: miR-155 is involved in immune system regulation and has been implicated in AD, PD and MS. In AD, miR-155 was found to target genes involved in Aβ production and neuroinflammation [105]. In PD, it was shown to regulate inflammation and dopaminergic neuron survival [106,107,108]. In MS, miR-155 was reported to modulate immune cell activation and the inflammatory response [109,110].
Table 1. miRNAs involved in the regulation of neuroprotective mechanisms.
Table 1. miRNAs involved in the regulation of neuroprotective mechanisms.
DiseasemiRNATargeted GenesPhysiological ProcessReference
Alzheimer’s diseasemiR-124BACE1Reduction of Aβ oligomers production and neuroprotection[81]
miR-34bVAMP2, SYT1, BCL2Regulation of presynaptic activity and anti-apoptotic mechanisms[111]
miR-30aBDNFRegulation of synaptic plasticity and neuroprotective mechanisms[112,113]
miR-34cP53, SIRT1Neuronal development
miR-125bBACE1Attenuated Aβ toxicity in Aβ-treated N2a cells via targeting BACE1[114]
miR-132No reportsPrevents the apoptosis of neurons and is involved in the regulation of synaptic plasticity, learning and memory, as well as in reducing tau hyperphosphorylation[19,115,116]
miR-124No reportsRegulates the hyperphosphorylation of the tau protein in cell cultures via the PI3K/AKT/GSK-3 pathway[94]
miR-29a
miR-29b
BACE1Regulation of APP and beta-site APP-cleaving enzyme 1 (BACE1)[117]
hsa-miR-483
hsa-miR-486
MAPK1/3Direct repression of Erk1/2 and reduction of tau phosphorylation[118,119]
miR-135a
miR-384
No reportsRepression of BACE-1 and APP-cleavage[120]
miR-342ANK3Regulation of neurogenesis and neuroprotection in an AD mouse model[121]
miR-20aBCL2, MEF2D, MAP3K12Regulation of gene expression during brain development.[122]
miR-329No reportsRegulation of activity-dependent dendritic outgrowth of hippocampal neurons
MiR-200aBACE1, PRKACBReduces Aβ accumulation and tau hyperphosphorylation, respectively[123]
Parkinson’s diseasemiR-150AKT3Downregulation of the proinflammatory cytokines IL-1β, IL-6 and TNF-α[114]
miR-124No reportsAttenuates microglia activation, improves survival of dopaminergic neurons and reduces α-synuclein aggregation[98,99]
miR-132
miR-92a
miR-27a
miR-148a
GLRXRegulates the activation and loss of microglial cells[111,124]
miR-146aNo reportsRegulation of anti-inflammatory mechanisms[104]
miR-153
miR-214
miR-34b/c
miR-7
No reportsDownregulation of α-synuclein expression, preventing its neurotoxicity[125,126,127,128]
miR-26bTAK1, TAB3Suppressing neuroinflammation by downregulating the activators of NF-Kβ[129]
miR-34aD1, SIRT1, BCL-2Neuronal development, brain ageing metabolic regulation p53/miR-34a/SIRT1 pathway[114]
miR-29No reportsRegulates T helper 1 (Th1) differentiation and targets the transcription factors T-bet and Eomes, resulting in the repression of IFN-γ production[130]
mir-29bBAD, BIM, BID, BIK/NBK, BNIP3, BLK, HRK, NOXA, and EGL-1Regulates neuronal survival by targeting genes in the pro-apoptotic BH3-only family to inhibit apoptosis[131]
miR-30eNLRP3Regulates neuroinflammation by reducing the inflammatory cytokines TNF-α, COX-2 and iNOS[132]
Amyotrophic Lateral SclerosismiR-128THY1p53 Pro-apoptotic pathway regulation[133,134]
miR-191BDNFNeuronal and immune cell apoptosis regulation[133]
miR-24BIM, PUMAThy1/ Thy2 balance regulation
miR-27a
miR-155
miR-142
No reportsRegulation and control of oxidative stress[135,136]
miR-300VASH2Neuron differentiation[137]
miR-450bSOX2, PTPRZ1Neuron differentiation and neurogenesis regulation
miR-34aXIAPProtective against oxidative stress-induced apoptosis through SIRT1[5,92]
Huntington’s diseasemiR-124aNo reportsRegulator of neuronal differentiation and morphology[100,138]
miR-128HTTNeuronal survival, metabolic pathways, particularly cholesterol (affected by mutant HTT)[139]
miR-122AACS, ADAM10, BCL2
miR-132ITPKBNeuronal development and survival[111,140]
miR-9HTT, CoRESTIncreases the expression of RE1 in leukocytes[89]
miR-140ADAM10Synaptic function regulation[90]
miR-34aD1, SIRT1, BCL-2Neuronal development and brain ageing metabolic regulation p53/miR-34a/SIRT1 pathway[141]
GlaucomamiR-92aKALRNAxonal guidance signaling, Ephrin B signaling, synaptogenesis signaling pathway[142]
miR-99bNo reportsRegulation of autophagy, senescence, neuroinflammation[143]
miR-125bLIN28BAdhesion, tight junctions and TGF-β signaling[141]
miR-192
miR-10a
miR-10b
No reportsNeurogenesis, aging, apoptosis and autophagy[144]
miR-375BDNF
miR-143LMO4Regulation of autophagy, apoptosis, senescence, neuroinflammation[145]
miR-221No reportsTGF-β and neurotrophin signaling[146]
miR-451aNo reportsAdhesion, tight junctions and TGF-β signaling[147]
miR-486LMCM, LMX1B, PTPN1TGF-β signaling regulation[146]
TXNRD2Antioxidant action of vitamin C, mitochondrial dysfunction, thioredoxin pathway, vitamin C transport
miR-124
miR-204
No reportsDevelopment and maintenance of retinal cells in adult mice.[95]
miR-29p53TGF-β signaling regulation and antioxidant effects.[97]
These are just a few examples of miRNAs that have shown therapeutic potential in multiple neurodegenerative diseases (Figure 2). The overlapping effects of these miRNAs suggest common underlying mechanisms or pathways involved in the pathogenesis of different neurodegenerative diseases. However, it is important to note that the specific targets and mechanisms of action of these miRNAs may vary in different diseases, and further research is needed to elucidate their precise roles and potential for therapeutic interventions.

5. Challenges and Potential Solutions for miRNA Therapy

miRNA-based therapy has become a promising strategy for the treatment of a variety of diseases. Nonetheless, the effects of miRNAs on multiple targets, the development of novel approaches based on their identification, the successful development of delivery methods and of chemical formulations to overcome the crossing of BBB and the description of their off-target effects are still a challenge for translating pre-clinical studies into clinical applications [148,149]. Several studies have already demonstrated possible alterations in signaling pathways in relation to the finding of miRNA target genes in widespread RNA-Seq data analysis, but additional investigations are needed to further develop miRNA-based therapies based on miRNA overexpression, functional inhibition or knockdown to control neurodegenerative injuries.
One of the main problems that need to be addressed while using miRNA-based therapies is to avoid miRNA degradation by RNases. Chemical modifications could solve this issue, but the short half-life of some constructs is still a challenge [148]. Another issue that needs to be considered is the time course of the miRNA effects. For example, the application of miR-124 increased neuronal survival in the ischemic brain, whereas a late administration did not show the same effect [149]. Some miRNAs’ “off-target” effects could also activate unwanted pathways that counteract their protective effects [149,150]. An example of the effects of miRNAs at multiple levels is provided by the miR-15-miR-16 cluster, which acts by downregulating multiple targeted apoptotic proteins, including BCL-2 and MLC1, affecting the same cancer hallmarks [151,152]. Despite these challenges in miRNA-based therapeutics, cell receptor- and tissue-specific targeted AAV have been demonstrated in numerous studies using preclinical models and even in current clinical trials for gene therapy, providing a promising way to deliver miRNA to specific sites [153]. These multiple target capacities of some miRNAs may also potentially boost the therapeutic effects compared to other RNA-based therapies, such as synthetic those using siRNAs or ASOs that act on target-specific genes.
Among the possibilities to overcome the challenges of using miRNA-based therapy, we can name (1) the use of ‘tiny’ antisense RNAs, which are short sequences of 7–8 nucleotides that target the 5′-seed of miRNAs and are able to inhibit an entire miRNA family sharing the same seed sequence [149]. Although the use of shorter sequences could increase the possible off-target effects, it was already demonstrated that targeting two miR-122 family members using locked nucleic acid (LNA)-modified antimiRs in vitro led to few off-target effects, with no measurable effects on the mRNA-targeted complementary sequence [154]; (2) applying the metronomic miRNA therapy, which is defined as a frequent administration of limited drug doses over a prolonged period, aiming to achieve a low active dose range with no excessive toxicity or immunological reactivity [149]; (3) combinatorial RNA therapy, which can also reduce the use of the required doses; for example, the application of miR-155 therapy was able to reestablish cancer cell sensitivity to chemotherapy [155], and, similarly, miR-34a therapy sensitized lung cells to radiotherapy [156]; (4) the use of small-molecule inhibitors of miRNAs (SMIRs), which can act on miRNAs at the transcriptional and post-transcriptional levels or even influence their processing to regulate specific miRNAs [157].
These are just a few examples of the application of miRNA therapy in combination with other approaches in order to overcome the current challenges. There is still an urge to better understand the multiple targeting actions of miRNAs and their effects, but the development of designed chemical formulations and new delivery methods, as well as the identification of new miRNA targets and the reduction of their off-target effects can lead to future successful clinical trials and effective miRNA-based treatments for several disorders, including neurodegenerative diseases.

6. Conclusions

RNA-Seq technologies provide a powerful tool for researchers to evaluate a wide volume of data that could lead to the identification of new biomarkers for numerous diseases, as well as of pathway alterations, protein network alterations and physiological events, including the regulation mediated by miRNAs, that are common between neurodegenerative disorders. These small molecules play a crucial role in diverse biological processes by regulating mRNA molecules, leading to mRNA degradation or translational repression. In the context of neurodegenerative diseases, miRNAs have emerged as promising therapeutic targets, with specific miRNAs showing dysregulation and implication in disease pathogenesis and progression. Modulating the expression or activity of these miRNAs holds great potential for novel therapeutic strategies. Some miRNAs, like miR-124, miR-9, miR-146a and miR-155, have demonstrated therapeutic effects in multiple neurodegenerative diseases, including Alzheimer’s, Parkinson’s, and Huntington’s diseases. These miRNAs target genes associated with key pathological processes, indicating shared molecular mechanisms among different neurodegenerative diseases. However, it is crucial to recognize that these miRNAs’ specific targets and mechanisms of action may vary across diseases, highlighting the complexity of miRNA-mediated regulation. While miRNAs represent a promising avenue for therapeutic intervention, more research and clinical trials are needed to fully explore their potential and translate these findings into effective treatments for neurodegenerative disorders, including neurodegenerative eye diseases like open-angle glaucoma.

Author Contributions

C.F.M.V. and H.P.-S. conceptualized the paper. C.F.M.V. drafted the manuscript. H.P.-S. and V.T.R. revised and finalized the work. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Ministério da Ciência, Tecnologia e Inovações (MCTI) and FAPERJ-APQ1.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Barnham, K.J.; Masters, C.L.; Bush, A.I. Neurodegenerative Diseases and Oxidatives Stress. Nat. Rev. Drug Discov. 2004, 3, 205–214. [Google Scholar] [CrossRef]
  2. Simonato, M.; Bennett, J.; Boulis, N.M.; Castro, M.G.; Fink, D.J.; Goins, W.F.; Gray, S.J.; Lowenstein, P.R.; Vandenberghe, L.H.; Wilson, T.J.; et al. Progress in Gene Therapy for Neurological Disorders. Nat. Rev. Neurol. 2013, 9, 277–291. [Google Scholar] [CrossRef]
  3. Seeley, W.W.; Crawford, R.K.; Zhou, J.; Miller, B.L.; Greicius, M.D. Neurodegenerative Diseases Target Large-Scale Human Brain Networks. Neuron 2009, 62, 42–52. [Google Scholar] [CrossRef]
  4. Matheus, F.C.; Rial, D.; Real, J.I.; Lemos, C.; Ben, J.; Guaita, G.O.; Pita, I.R.; Sequeira, A.C.; Pereira, F.C.; Walz, R.; et al. Decreased Synaptic Plasticity in the Medial Prefrontal Cortex Underlies Short-Term Memory Deficits in 6-OHDA-Lesioned Rats. Behav. Brain Res. 2016, 301, 43–54. [Google Scholar] [CrossRef] [PubMed]
  5. Singh, P.; Hanson, P.S.; Morris, C.M. SIRT1 Ameliorates Oxidative Stress Induced Neural Cell Death and Is Down-Regulated in Parkinson’s Disease. BMC Neurosci. 2017, 18, 46. [Google Scholar] [CrossRef] [PubMed]
  6. Simões-Pires, E.N.; Ferreira, S.T.; Linden, R. Roles of Glutamate Receptors in a Novel in Vitro Model of Early, Comorbid Cerebrovascular, and Alzheimer’s Diseases. J. Neurochem. 2021, 156, 539–552. [Google Scholar] [CrossRef]
  7. Gupta, N.; Fong, J.; Ang, L.C.; Yücel, Y.H. Retinal Tau Pathology in Human Glaucomas. Can. J. Ophthalmol. 2008, 43, 53–60. [Google Scholar] [CrossRef]
  8. Moon, J.Y.; Kim, H.J.; Park, Y.H.; Park, T.K.; Park, E.C.; Kim, C.Y.; Lee, S.H. Association between Open-Angle Glaucoma and the Risks of Alzheimer’s and Parkinson’s Diseases in South Korea: A 10-Year Nationwide Cohort Study. Sci. Rep. 2018, 24, 11161. [Google Scholar] [CrossRef] [PubMed]
  9. Weinreb, R.N.; Aung, T.; Medeiros, F.A. The Pathophysiology and Treatment of Glaucoma: A Review. JAMA 2014, 311, 1901–1911. [Google Scholar] [CrossRef]
  10. Dias, M.S.; Luo, X.; Ribas, V.T.; Petrs-Silva, H.; Koch, J.C. The Role of Axonal Transport in Glaucoma. Int. J. Mol. Sci. 2022, 23, 3935. [Google Scholar] [CrossRef] [PubMed]
  11. WHO. World Report on Vision; World Health Organization: Geneva, Switzerland, 2019; Volume 214, ISBN 9789241516570. [Google Scholar]
  12. Swenor, B.K.; Ehrlich, J.R. Ageing and Vision Loss: Looking to the Future. Lancet Glob. Health 2021, 9, e385–e386. [Google Scholar] [CrossRef]
  13. Bayer, A.U.; Keller, O.N.; Ferrari, F.; Maag, K.P. Association of Glaucoma with Neurodegenerative Diseases with Apoptotic Cell Death: Alzheimer’s Disease and Parkinson’s Disease. Am. J. Ophthalmol. 2002, 133, 135–137. [Google Scholar] [CrossRef] [PubMed]
  14. Chrysostomou, V.; Rezania, F.; Trounce, I.A.; Crowston, J.G. Oxidative Stress and Mitochondrial Dysfunction in Glaucoma. Curr. Opin. Pharmacol. 2013, 13, 12–15. [Google Scholar] [CrossRef] [PubMed]
  15. Llorens, F.; Thüne, K.; Martí, E.; Kanata, E.; Dafou, D.; Díaz-Lucena, D.; Vivancos, A.; Shomroni, O.; Zafar, S.; Schmitz, M.; et al. Regional and Subtype-Dependent MiRNA Signatures in Sporadic Creutzfeldt-Jakob Disease Are Accompanied by Alterations in MiRNA Silencing Machinery and Biogenesis. PLoS Pathog. 2018, 14, e1006802. [Google Scholar] [CrossRef]
  16. Kumar, A.; Kopra, J.; Varendi, K.; Porokuokka, L.L.; Panhelainen, A.; Kuure, S.; Marshall, P.; Karalija, N.; Härma, M.A.; Vilenius, C.; et al. GDNF Overexpression from the Native Locus Reveals Its Role in the Nigrostriatal Dopaminergic System Function. PLoS Genet. 2015, 11, e1005710. [Google Scholar] [CrossRef]
  17. Islam, M.R.; Kaurani, L.; Berulava, T.; Heilbronner, U.; Budde, M.; Centeno, T.P.; Elerdashvili, V.; Zafieriou, M.; Benito, E.; Sertel, S.; et al. A MicroRNA Signature That Correlates with Cognition and Is a Target against Cognitive Decline. EMBO Mol. Med. 2021, 13, e13659. [Google Scholar] [CrossRef] [PubMed]
  18. Cogswell, J.P.; Ward, J.; Taylor, I.A.; Waters, M.; Shi, Y.; Cannon, B.; Kelnar, K.; Kemppainen, J.; Brown, D.; Chen, C.; et al. Identification of MiRNA Changes in Alzheimer’s Disease Brain and CSF Yields Putative Biomarkers and Insights into Disease Pathways. J. Alzheimer’s Dis. 2008, 14, 27–41. [Google Scholar] [CrossRef]
  19. Lau, P.; Bossers, K.; Janky, R.; Salta, E.; Frigerio, C.S.; Barbash, S.; Rothman, R.; Sierksma, A.S.R.; Thathiah, A.; Greenberg, D.; et al. Alteration of the MicroRNA Network during the Progression of Alzheimer’s Disease. EMBO Mol. Med. 2013, 5, 1613–1634. [Google Scholar] [CrossRef]
  20. Kanach, C.; Blusztajn, J.K.; Fischer, A.; Delalle, I. MicroRNAs as Candidate Biomarkers for Alzheimer’s Disease. Non-Coding RNA 2021, 7, 8. [Google Scholar] [CrossRef]
  21. Miñones-Moyano, E.; Porta, S.; Escaramís, G.; Rabionet, R.; Iraola, S.; Kagerbauer, B.; Espinosa-Parrilla, Y.; Ferrer, I.; Estivill, X.; Martí, E. MicroRNA Profiling of Parkinson’s Disease Brains Identifies Early Downregulation of MiR-34b/c Which Modulate Mitochondrial Function. Hum. Mol. Genet. 2011, 20, 3067–3078. [Google Scholar] [CrossRef]
  22. Martí, E.; Pantano, L.; Bañez-Coronel, M.; Llorens, F.; Miñones-Moyano, E.; Porta, S.; Sumoy, L.; Ferrer, I.; Estivill, X. A Myriad of MiRNA Variants in Control and Huntington’s Disease Brain Regions Detected by Massively Parallel Sequencing. Nucleic Acids Res. 2010, 38, 7219–7235. [Google Scholar] [CrossRef] [PubMed]
  23. Jindal, V. Glaucoma: An Extension of Various Chronic Neurodegenerative Disorders. Mol. Neurobiol. 2013, 48, 186–189. [Google Scholar] [CrossRef]
  24. Marchesi, N.; Fahmideh, F.; Boschi, F.; Pascale, A.; Barbieri, A. Ocular Neurodegenerative Diseases: Interconnection between Retina and Cortical Areas. Cells 2021, 10, 2394. [Google Scholar] [CrossRef]
  25. Snyder, P.J.; Alber, J.; Alt, C.; Bain, L.J.; Bouma, B.E.; Bouwman, F.H.; DeBuc, D.C.; Campbell, M.C.W.; Carrillo, M.C.; Chew, E.Y.; et al. Retinal Imaging in Alzheimer’s and Neurodegenerative Diseases. Alzheimer’s Dement. 2021, 17, 103–111. [Google Scholar] [CrossRef] [PubMed]
  26. Wareham, L.K.; Calkins, D.J. The Neurovascular Unit in Glaucomatous Neurodegeneration. Front. Cell Dev. Biol. 2020, 8, 452. [Google Scholar] [CrossRef] [PubMed]
  27. Calkins, D.J. Adaptive Responses to Neurodegenerative Stress in Glaucoma. Prog. Retin. Eye Res. 2021, 84, 100953. [Google Scholar] [CrossRef]
  28. Weinreb, R.N. Ocular Hypertension: Defining Risks and Clinical Options. Am. J. Ophthalmol. 2004, 138, S1–S2. [Google Scholar] [CrossRef]
  29. Tamm, E.R.; Ethier, C.R.; Dowling, J.E.; Downs, C.; Ellisman, M.H.; Fisher, S.; Fortune, B.; Fruttiger, M.; Jakobs, T.; Lewis, G.; et al. Biological Aspects of Axonal Damage in Glaucoma: A Brief Review. Exp. Eye Res. 2017, 157, 5–12. [Google Scholar] [CrossRef]
  30. Lawlor, M.; Danesh-Meyer, H.; Levin, L.A.; Davagnanam, I.; De Vita, E.; Plant, G.T. Glaucoma and the Brain: Trans-Synaptic Degeneration, Structural Change, and Implications for Neuroprotection. Surv. Ophthalmol. 2018, 63, 296–306. [Google Scholar] [CrossRef]
  31. Rojas, P.; Ramírez, A.I.; Fernández-Albarral, J.A.; López-Cuenca, I.; Salobrar-García, E.; Cadena, M.; Elvira-Hurtado, L.; Salazar, J.J.; de Hoz, R.; Ramírez, J.M. Amyotrophic Lateral Sclerosis: A Neurodegenerative Motor Neuron Disease With Ocular Involvement. Front. Neurosci. 2020, 14, 566858. [Google Scholar] [CrossRef]
  32. Soldatov, V.O.; Kukharsky, M.S.; Belykh, A.E.; Sobolev, A.M.; Deykin, A.V. Retinal Damage in Amyotrophic Lateral Sclerosis: Underlying Mechanisms. Eye Brain 2021, 3, 131–146. [Google Scholar] [CrossRef] [PubMed]
  33. O’Bryhim, B.E.; Apte, R.S.; Kung, N.; Coble, D.; Van Stavern, G.P. Association of Preclinical Alzheimer Disease with Optical Coherence Tomographic Angiography Findings. JAMA Ophthalmol. 2018, 136, 1242–1248. [Google Scholar] [CrossRef] [PubMed]
  34. Huang, L.; Zhang, D.; Ji, J.; Wang, Y.; Zhang, R. Central Retina Changes in Parkinson’s Disease: A Systematic Review and Meta-Analysis. J. Neurol. 2021, 268, 4646–4654. [Google Scholar] [CrossRef] [PubMed]
  35. McKinnon, S.J.; Lehman, D.M.; Kerrigan-Baumrind, L.A.; Merges, C.A.; Pease, M.E.; Kerrigan, D.F.; Ransom, N.L.; Tahzib, N.G.; Reitsamer, H.A.; Levkovitch-Verbin, H.; et al. Caspase Activation and Amyloid Precursor Protein Cleavage in Rat Ocular Hypertension. Investig. Ophthalmol. Vis. Sci. 2002, 43, 1077–1087. [Google Scholar]
  36. Indrieri, A.; Pizzarelli, R.; Franco, B.; De Leonibus, E. Dopamine, Alpha-Synuclein, and Mitochondrial Dysfunctions in Parkinsonian Eyes. Front. Neurosci. 2020, 14, 567129. [Google Scholar] [CrossRef]
  37. Sugiyama, K.; Tomita, G.; Kitazawa, Y.; Onda, E.; Shinohara, H. Ki Ho Park The Associations of Optic Disc Hemorrhage with Retinal Nerve Fiber Layer Defect and Peripapillary Atrophy in Normal-Tension Glaucoma. Ophthalmology 1997, 104, 1926–1933. [Google Scholar] [CrossRef]
  38. Schmidl, D.; Resch, H.; Rensch, F.; Hommer, A.; Vass, C.; Luksch, A.; Garhofer, G.; Jonas, J.; Schmetterer, L. Correlation of Optic Disc Morphology and Ocular Perfusion Parameters in Patients with Primary Open Angle Glaucoma. Acta Ophthalmol. 2008, 86, e544–e549. [Google Scholar] [CrossRef]
  39. Nitta, K.; Sugiyama, K.; Wajima, R.; Tachibana, G.; Yamada, Y. Associations between Changes in Radial Peripapillary Capillaries and Occurrence of Disc Hemorrhage in Normal-Tension Glaucoma. Graefe’s Arch. Clin. Exp. Ophthalmol. 2019, 257, 1963–1970. [Google Scholar] [CrossRef]
  40. Wax, M.B. The Case for Autoimmunity in Glaucoma. Exp. Eye Res. 2011, 93, 187–190. [Google Scholar] [CrossRef]
  41. Yin, Y.; Cui, Q.; Gilbert, H.Y.; Yang, Y.; Yang, Z.; Berlinicke, C.; Li, Z.; Zaverucha-do-Valle, C.; He, H.; Petkovag, V.; et al. Oncomodulin Links Inflammation to Optic Nerve Regeneration. Proc. Natl. Acad. Sci. USA 2009, 106, 19587–19592. [Google Scholar] [CrossRef]
  42. Yin, Y.; Cui, Q.; Li, Y.; Irwin, N.; Fischer, D.; Harvey, A.R.; Benowitz, L.I. Macrophage-Derived Factors Stimulate Optic Nerve Regeneration. J. Neurosci. 2003, 23, 2284–2293. [Google Scholar] [CrossRef] [PubMed]
  43. Yin, Y.; Henzl, M.T.; Lorber, B.; Nakazawa, T.; Thomas, T.T.; Jiang, F.; Langer, R.; Benowitz, L.I. Oncomodulin Is a Macrophage-Derived Signal for Axon Regeneration in Retinal Ganglion Cells. Nat. Neurosci. 2006, 9, 843–852. [Google Scholar] [CrossRef] [PubMed]
  44. Calon, F.; Lim, G.P.; Yang, F.; Morihara, T.; Teter, B.; Ubeda, O.; Rostaing, P.; Triller, A.; Salem, N.; Ashe, K.H.; et al. Docosahexaenoic Acid Protects from Dendritic Pathology in an Alzheimer’s Disease Mouse Model. Neuron 2004, 43, 633–645. [Google Scholar] [CrossRef] [PubMed]
  45. Calon, F.; Lim, G.P.; Morihara, T.; Yang, F.; Ubeda, O.; Salem, N.; Frautschy, S.A.; Cole, G.M. Dietary N-3 Polyunsaturated Fatty Acid Depletion Activates Caspases and Decreases NMDA Receptors in the Brain of a Transgenic Mouse Model of Alzheimer’s Disease. Eur. J. Neurosci. 2005, 22, 617–626. [Google Scholar] [CrossRef] [PubMed]
  46. Bourre, J.M. Effects of Nutrients (in Food) on the Structure and Function of the Nervous System: Update on Dietary Requirements for Brain. Part 2: Macronutrients. J. Nutr. Health Aging 2006, 10, 386–399. [Google Scholar]
  47. Bousquet, M.; Saint-Pierre, M.; Julien, C.; Salem, N.; Cicchetti, F.; Calon, F. Beneficial Effects of Dietary Omega-3 Polyunsaturated Fatty Acid on Toxin-induced Neuronal Degeneration in an Animal Model of Parkinson’s Disease. FASEB J. 2008, 22, 1213–1225. [Google Scholar] [CrossRef]
  48. Spain, R.I.; Liu, L.; Zhang, X.; Jia, Y.; Tan, O.; Bourdette, D.; Huang, D. Optical Coherence Tomography Angiography Enhances the Detection of Optic Nerve Damage in Multiple Sclerosis. Br. J. Ophthalmol. 2018, 102, 520–524. [Google Scholar] [CrossRef]
  49. Faissner, S.; Gold, R. Progressive Multiple Sclerosis: Latest Therapeutic Developments and Future Directions. Ther. Adv. Neurol. Disord. 2019, 12, 1756286419878323. [Google Scholar] [CrossRef]
  50. Vitek, M.P.; Brown, C.M.; Colton, C.A. APOE Genotype-Specific Differences in the Innate Immune Response. Neurobiol. Aging 2009, 30, 1350–1360. [Google Scholar] [CrossRef]
  51. Tulloch, J.; Leong, L.; Thomson, Z.; Chen, S.; Lee, E.G.; Keene, C.D.; Millard, S.P.; Yu, C.E. Glia-Specific APOE Epigenetic Changes in the Alzheimer’s Disease Brain. Brain Res. 2018, 1698, 179–186. [Google Scholar] [CrossRef]
  52. Chai, A.B.; Lam, H.H.J.; Kockx, M.; Gelissen, I.C. Apolipoprotein E Isoform-Dependent Effects on the Processing of Alzheimer’s Amyloid-β. Biochim. Biophys. Acta (BBA) Mol. Cell Biol. Lipids 2021, 1866, 158980. [Google Scholar] [CrossRef] [PubMed]
  53. Margeta, M.A.; Letcher, S.M.; Igo, R.P.; Cooke Bailey, J.N.; Pasquale, L.R.; Haines, J.L.; Butovsky, O.; Wiggs, J.L. Association of APOE with Primary Open-Angle Glaucoma Suggests a Protective Effect for APOE Ε4. Investig. Ophthalmol. Vis. Sci. 2020, 61, 3. [Google Scholar] [CrossRef] [PubMed]
  54. Davis, A.A.; Inman, C.E.; Wargel, Z.M.; Dube, U.; Freeberg, B.M.; Galluppi, A.; Haines, J.N.; Dhavale, D.D.; Miller, R.; Choudhury, F.A.; et al. APOE Genotype Regulates Pathology and Disease Progression in Synucleinopathy. Sci. Transl. Med. 2020, 12, eaay3069. [Google Scholar] [CrossRef] [PubMed]
  55. Narendra, D.P.; Jin, S.M.; Tanaka, A.; Suen, D.F.; Gautier, C.A.; Shen, J.; Cookson, M.R.; Youle, R.J. PINK1 Is Selectively Stabilized on Impaired Mitochondria to Activate Parkin. PLoS Biol. 2010, 8, e1000298. [Google Scholar] [CrossRef]
  56. Kitada, T.; Asakawa, S.; Hattori, N.; Matsumine, H.; Yamamura, Y.; Minoshima, S.; Yokochi, M.; Mizuno, Y.; Shimizu, N. Mutations in the Parkin Gene Cause Autosomal Recessive Juvenile Parkinsonism. Nature 1998, 392, 605–608. [Google Scholar] [CrossRef]
  57. Valente, E.M.; Bentivoglio, A.R.; Dixon, P.H.; Ferraris, A.; Lalongo, T.; Frontali, M.; Albanese, A.; Wood, N.W. Localization of a Novel Locus for Autosomal Recessive Early-Onset Parkinsonism, PARK6, on Human Chromosome 1p35–P36. Am. J. Hum. Genet. 2001, 68, 895–900. [Google Scholar] [CrossRef]
  58. Malpartida, A.B.; Williamson, M.; Narendra, D.P.; Wade-Martins, R.; Ryan, B.J. Mitochondrial Dysfunction and Mitophagy in Parkinson’s Disease: From Mechanism to Therapy. Trends Biochem. Sci. 2021, 46, 329–343. [Google Scholar] [CrossRef]
  59. Tribble, J.R.; Vasalauskaite, A.; Redmond, T.; Young, R.D.; Hassan, S.; Fautsch, M.P.; Sengpiel, F.; Williams, P.A.; Morgan, J.E. Midget Retinal Ganglion Cell Dendritic and Mitochondrial Degeneration Is an Early Feature of Human Glaucoma. Brain Commun. 2019, 1, fcz035. [Google Scholar] [CrossRef]
  60. Takihara, Y.; Inatani, M.; Eto, K.; Inoue, T.; Kreymerman, A.; Miyake, S.; Ueno, S.; Nagaya, M.; Nakanishi, A.; Iwao, K.; et al. In Vivo Imaging of Axonal Transport of Mitochondria in the Diseased and Aged Mammalian CNS. Proc. Natl. Acad. Sci. USA 2015, 112, 10515–10520. [Google Scholar] [CrossRef]
  61. Hvozda Arana, A.G.; Lasagni Vitar, R.M.; Reides, C.G.; Calabró, V.; Marchini, T.; Lerner, S.F.; Evelson, P.A.; Ferreira, S.M. Mitochondrial Function Is Impaired in the Primary Visual Cortex in an Experimental Glaucoma Model. Arch. Biochem. Biophys. 2021, 701, 108815. [Google Scholar] [CrossRef]
  62. Redina, O.E.; Babenko, V.N. Advances of Brain Transcriptomics. Genes 2022, 13, 1831. [Google Scholar] [CrossRef] [PubMed]
  63. Wang, Z.; Gerstein, M.; Snyder, M. RNA-Seq: A Revolutionary Tool for Transcriptomics. Nat. Rev. Genet. 2009, 10, 57–63. [Google Scholar] [CrossRef] [PubMed]
  64. Ham, S.; Lee, S.J.V. Advances in Transcriptome Analysis of Human Brain Aging. Exp. Mol. Med. 2020, 52, 1787–1797. [Google Scholar] [CrossRef] [PubMed]
  65. Lowe, R.; Shirley, N.; Bleackley, M.; Dolan, S.; Shafee, T. Transcriptomics Technologies. PLoS Comput. Biol. 2017, 13, e1005457. [Google Scholar] [CrossRef] [PubMed]
  66. Arneson, D.; Zhang, Y.; Yang, X.; Narayanan, M. Shared Mechanisms among Neurodegenerative Diseases: From Genetic Factors to Gene Networks. J. Genet. 2018, 97, 795–806. [Google Scholar] [CrossRef] [PubMed]
  67. Li, M.D.; Burns, T.C.; Morgan, A.A.; Khatri, P. Integrated Multi-Cohort Transcriptional Meta-Analysis of Neurodegenerative Diseases. Acta Neuropathol. Commun. 2014, 2, 93. [Google Scholar] [CrossRef]
  68. Lake, J.; Storm, C.S.; Makarious, M.B.; Bandres-Ciga, S. Genetic and Transcriptomic Biomarkers in Neurodegenerative Diseases: Current Situation and the Road Ahead. Cells 2021, 10, 1030. [Google Scholar] [CrossRef]
  69. Gatz, M.; Reynolds, C.A.; Fratiglioni, L.; Johansson, B.; Mortimer, J.A.; Berg, S.; Fiske, A.; Pedersen, N.L. Role of Genes and Environments for Explaining Alzheimer Disease. Arch. Gen. Psychiatry 2006, 63, 168–174. [Google Scholar] [CrossRef]
  70. Van Cauwenberghe, C.; Van Broeckhoven, C.; Sleegers, K. The Genetic Landscape of Alzheimer Disease: Clinical Implications and Perspectives. Genet. Med. 2016, 18, 421–430. [Google Scholar] [CrossRef]
  71. Hamza, T.H.; Payami, H. The Heritability of Risk and Age at Onset of Parkinson’s Disease after Accounting for Known Genetic Risk Factors. J. Hum. Genet. 2010, 55, 241–243. [Google Scholar] [CrossRef]
  72. Bertram, L.; Tanzi, R.E. The Genetic Epidemiology of Neurodegenerative Disease. J. Clin. Investig. 2005, 115, 1449–1457. [Google Scholar] [CrossRef]
  73. Pasinelli, P.; Brown, R.H. Molecular Biology of Amyotrophic Lateral Sclerosis: Insights from Genetics. Nat. Rev. Neurosci. 2006, 7, 710–723. [Google Scholar] [CrossRef]
  74. Khawaja, A.P.; Viswanathan, A.C. Are we ready for genetic testing for primary open-angle glaucoma? Eye 2018, 32, 877–883. [Google Scholar] [CrossRef] [PubMed]
  75. Wang, J.; Struebing, F.L.; Geisert, E.E. Commonalities of Optic Nerve Injury and Glaucoma-Induced Neurodegeneration: Insights from Transcriptome-Wide Studies. Exp. Eye Res. 2021, 207, 108571. [Google Scholar] [CrossRef]
  76. Li, P.; Nie, Y.; Yu, J. An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases. PLoS ONE 2015, 10, e0143045. [Google Scholar] [CrossRef] [PubMed]
  77. 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] [PubMed]
  78. Bartel, D.P. MicroRNAs: Genomics, Biogenesis, Mechanism, and Function. Cell 2004, 116, 281–297. [Google Scholar] [CrossRef]
  79. Ha, M.; Kim, V.N. Regulation of MicroRNA Biogenesis. Nat. Rev. Mol. Cell Biol. 2014, 15, 509–524. [Google Scholar] [CrossRef]
  80. Nhung Nguyen, T.P.; Kumar, M.; Fedele, E.; Bonanno, G.; Bonifacino, T. MicroRNA Alteration, Application as Biomarkers, and Therapeutic Approaches in Neurodegenerative Diseases. Int. J. Mol. Sci. 2022, 23, 4718. [Google Scholar] [CrossRef]
  81. Fang, M.; Wang, J.; Zhang, X.; Geng, Y.; Hu, Z.; Rudd, J.A.; Ling, S.; Chen, W.; Han, S. The MiR-124 Regulates the Expression of BACE1/β-Secretase Correlated with Cell Death in Alzheimer’s Disease. Toxicol. Lett. 2012, 209, 94–105. [Google Scholar] [CrossRef]
  82. Kou, X.; Chen, D.; Chen, N. The Regulation of MicroRNAs in Alzheimer’s Disease. Front. Neurol. 2020, 11, 288. [Google Scholar] [CrossRef] [PubMed]
  83. Cosín-Tomás, M.; Antonell, A.; Lladó, A.; Alcolea, D.; Fortea, J.; Ezquerra, M.; Lleó, A.; Martí, M.J.; Pallàs, M.; Sanchez-Valle, R.; et al. Plasma MiR-34a-5p and MiR-545-3p as Early Biomarkers of Alzheimer’s Disease: Potential and Limitations. Mol. Neurobiol. 2017, 54, 5550–5562. [Google Scholar] [CrossRef] [PubMed]
  84. Xu, Y.; Chen, P.; Wang, X.; Yao, J.; Zhuang, S. MiR-34a Deficiency in APP/PS1 Mice Promotes Cognitive Function by Increasing Synaptic Plasticity via AMPA and NMDA Receptors. Neurosci. Lett. 2018, 670, 94–104. [Google Scholar] [CrossRef]
  85. Li, H.; Yu, L.; Li, M.; Chen, X.; Tian, Q.; Jiang, Y.; Li, N. MicroRNA-150 Serves as a Diagnostic Biomarker and Is Involved in the Inflammatory Pathogenesis of Parkinson’s Disease. Mol. Genet. Genom. Med. 2020, 8, e1189. [Google Scholar] [CrossRef] [PubMed]
  86. Li, S.; Lei, Z.; Sun, T. The Role of MicroRNAs in Neurodegenerative Diseases: A Review. Cell Biol. Toxicol. 2023, 39, 53–83. [Google Scholar] [CrossRef] [PubMed]
  87. Higgs, P.G.; Lehman, N. The RNA World: Molecular cooperation at the origins of life. Nat. Rev. Genet. 2015, 16, 7–17. [Google Scholar] [CrossRef]
  88. Zimprich, A.; Biskup, S.; Leitner, P.; Lichtner, P.; Farrer, M.; Lincoln, S.; Gasser, T. Mutations in LRRK2 cause autosomal-dominant parkinsonism with pleomorphic pathology. Neuron 2004, 44, 601–607. [Google Scholar] [CrossRef]
  89. Chang, K.H.; Wu, Y.R.; Chen, C.M. Down-Regulation of MiR-9∗ in the Peripheral Leukocytes of Huntington’s Disease Patients. Orphanet J. Rare Dis. 2017, 12, 185. [Google Scholar] [CrossRef]
  90. Reed, E.R.; Latourelle, J.C.; Bockholt, J.H.; Bregu, J.; Smock, J.; Paulsen, J.S.; Myers, R.H. MicroRNAs in CSF as Prodromal Biomarkers for Huntington Disease in the PREDICT-HD Study. Neurology 2018, 90, e264–e272. [Google Scholar] [CrossRef]
  91. Liu, T.; Im, W.; Mook-Jung, I.; Kim, M. MicroRNA-124 Slows down the Progression of Huntington’s Disease by Promoting Neurogenesis in the Striatum. Neural Regen. Res. 2015, 10, 786–791. [Google Scholar] [CrossRef]
  92. Yamakuchi, M.; Ferlito, M.; Lowenstein, C.J. MiR-34a Repression of SIRT1 Regulates Apoptosis. Proc. Natl. Acad. Sci. USA 2008, 105, 13421–13426. [Google Scholar] [CrossRef] [PubMed]
  93. De Luna, N.; Turon-Sans, J.; Cortes-Vicente, E.; Carrasco-Rozas, A.; Illán-Gala, I.; Dols-Icardo, O.; Clarimón, J.; Lleó, A.; Gallardo, E.; Illa, I.; et al. Downregulation of MiR-335-5P in Amyotrophic Lateral Sclerosis Can Contribute to Neuronal Mitochondrial Dysfunction and Apoptosis. Sci. Rep. 2020, 10, 4308. [Google Scholar] [CrossRef] [PubMed]
  94. Kang, Q.; Xiang, Y.; Li, D.; Liang, J.; Zhang, X.; Zhou, F.; Qiao, M.; Nie, Y.; He, Y.; Cheng, J.; et al. MiR-124-3p Attenuates Hyperphosphorylation of Tau Proteininduced Apoptosis via Caveolin-1-PI3K/Akt/GSK3β Pathway in N2a/APP695swe Cells. Oncotarget 2017, 8, 24314–24326. [Google Scholar] [CrossRef]
  95. Bereimipour, A.; Najafi, H.; Mirsane, E.S.; Moradi, S.; Satarian, L. Roles of miR-204 in retinal development and maintenance. Exp. Cell Res. 2021, 406, 112737. [Google Scholar] [CrossRef]
  96. Shahriari, F.; Satarian, L.; Moradi, S.; Zarchi, A.S.; Günther, S.; Kamal, A.; Totonchi, M.; Mowla, S.-J.; Braun, T.; Baharvand, H. MicroRNA profiling reveals important functions of miR-125b and let-7a during human retinal pigment epithelial cell differentiation. Exp. Eye Res. 2020, 190, 107883. [Google Scholar] [CrossRef]
  97. Luna, C.; Li, G.; Qiu, J.; Epstein, D.L.; Gonzalez, P. Role of miR-29b on the regulation of the extracellular matrix in human trabecular meshwork cells under chronic oxidative stress. Mol. Vis. 2009, 15, 2488–2497. [Google Scholar] [PubMed]
  98. Yao, L.; Ye, Y.; Mao, H.; Lu, F.; He, X.; Lu, G.; Zhang, S. MicroRNA-124 Regulates the Expression of MEKK3 in the Inflammatory Pathogenesis of Parkinson’s Disease. J. Neuroinflamm. 2018, 15, 13. [Google Scholar] [CrossRef]
  99. Slota, J.A.; Booth, S.A. MicroRNAs in Neuroinflammation: Implications in Disease Pathogenesis, Biomarker Discovery and Therapeutic Applications. Non-Coding RNA 2019, 5, 35. [Google Scholar] [CrossRef]
  100. Das, E.; Jana, N.R.; Bhattacharyya, N.P. MicroRNA-124 Targets CCNA2 and Regulates Cell Cycle in STHdhQ111/HdhQ111 Cells. Biochem. Biophys. Res. Commun. 2013, 437, 217–224. [Google Scholar] [CrossRef]
  101. Saraiva, C.; Esteves, M.; Bernardino, L. MicroRNA: Basic Concepts and Implications for Regeneration and Repair of Neurodegenerative Diseases. Biochem. Pharmacol. 2017, 141, 118–131. [Google Scholar] [CrossRef]
  102. Sierksma, A.; Lu, A.; Salta, E.; Vanden Eynden, E.; Callaerts-Vegh, Z.; D’Hooge, R.; Blum, D.; Buée, L.; Fiers, M.; De Strooper, B. Deregulation of Neuronal MiRNAs Induced by Amyloid-β or TAU Pathology 11 Medical and Health Sciences 1109 Neurosciences. Mol. Neurodegener. 2018, 13, 54. [Google Scholar] [CrossRef] [PubMed]
  103. Caggiu, E.; Paulus, K.; Mameli, G.; Arru, G.; Sechi, G.P.; Sechi, L.A. Differential Expression of MiRNA 155 and MiRNA 146a in Parkinson’s Disease Patients. eNeurologicalSci 2018, 13, 1–4. [Google Scholar] [CrossRef] [PubMed]
  104. Martin, N.A.; Hyrlov, K.H.; Elkjaer, M.L.; Thygesen, E.K.; Wlodarczyk, A.; Elbaek, K.J.; Aboo, C.; Okarmus, J.; Benedikz, E.; Reynolds, R.; et al. Absence of MiRNA-146a Differentially Alters Microglia Function and Proteome. Front. Immunol. 2020, 11, 1110. [Google Scholar] [CrossRef]
  105. Aloi, M.S.; Prater, K.E.; Sánchez, R.E.A.; Beck, A.; Pathan, J.L.; Davidson, S.; Wilson, A.; Keene, C.D.; de la Iglesia, H.; Jayadev, S.; et al. Microglia Specific Deletion of MiR-155 in Alzheimer’s Disease Mouse Models Reduces Amyloid-β Pathology but Causes Hyperexcitability and Seizures. J. Neuroinflamm. 2023, 20, 60. [Google Scholar] [CrossRef] [PubMed]
  106. Thome, A.D.; Harms, A.S.; Volpicelli-Daley, L.A.; Standaert, D.G. MicroRNA-155 Regulates Alpha-Synuclein-Induced Inflammatory Responses in Models of Parkinson Disease. J. Neurosci. 2016, 6, 2383–2390. [Google Scholar] [CrossRef] [PubMed]
  107. Lv, R.; Du, L.; Zhou, F.; Yuan, X.; Liu, X.; Zhang, L. Rosmarinic Acid Alleviates Inflammation, Apoptosis, and Oxidative Stress through Regulating MiR-155-5p in a Mice Model of Parkinson’s Disease. ACS Chem. Neurosci. 2020, 11, 3259–3266. [Google Scholar] [CrossRef]
  108. Briggs, C.E.; Wang, Y.; Kong, B.; Woo, T.U.W.; Iyer, L.K.; Sonntag, K.C. Midbrain Dopamine Neurons in Parkinson’s Disease Exhibit a Dysregulated MiRNA and Target-Gene Network. Brain Res. 2015, 1618, 111–121. [Google Scholar] [CrossRef]
  109. Essandoh, K.; Li, Y.; Huo, J.; Fan, G.C. MiRNA-Mediated Macrophage Polarization and Its Potential Role in the Regulation of Inflammatory Response. Shock 2016, 46, 122–131. [Google Scholar] [CrossRef]
  110. Zingale, V.D.; Gugliandolo, A.; Mazzon, E. MiR-155: An Important Regulator of Neuroinflammation. Int. J. Mol. Sci. 2021, 23, 90. [Google Scholar] [CrossRef]
  111. Cha, D.J.; Mengel, D.; Mustapic, M.; Liu, W.; Selkoe, D.J.; Kapogiannis, D.; Galasko, D.; Rissman, R.A.; Bennett, D.A.; Walsh, D.M. MiR-212 and MiR-132 Are Downregulated in Neurally Derived Plasma Exosomes of Alzheimer’s Patients. Front. Neurosci. 2019, 13, 1208. [Google Scholar] [CrossRef]
  112. Jain, G.; Stuendl, A.; Rao, P.; Berulava, T.; Pena Centeno, T.; Kaurani, L.; Burkhardt, S.; Delalle, I.; Kornhuber, J.; Hüll, M.; et al. A Combined MiRNA–PiRNA Signature to Detect Alzheimer’s Disease. Transl. Psychiatry 2019, 9, 250. [Google Scholar] [CrossRef] [PubMed]
  113. Rokavec, M.; Li, H.; Jiang, L.; Hermeking, H. The P53/MiR-34 Axis in Development and Disease. J. Mol. Cell Biol. 2014, 6, 214–230. [Google Scholar] [CrossRef] [PubMed]
  114. Li, P.; Xu, Y.; Wang, B.; Huang, J.; Li, Q. MiR-34a-5p and MiR-125b-5p Attenuate Aβ-Induced Neurotoxicity through Targeting BACE1. J. Neurol. Sci. 2020, 413, 116793. [Google Scholar] [CrossRef] [PubMed]
  115. Hernandez-Rapp, J.; Rainone, S.; Hébert, S.S. MicroRNAs Underlying Memory Deficits in Neurodegenerative Disorders. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 2017, 73, 79–86. [Google Scholar] [CrossRef]
  116. Tan, Z.; Chen, Y.; Xie, W.; Liu, X.; Zhu, Y.; Zhu, Y. Nimodipine Attenuates Tau Phosphorylation at Ser396 via MiR-132/GSK-3β Pathway in Chronic Cerebral Hypoperfusion Rats. Eur. J. Pharmacol. 2018, 819, 1–8. [Google Scholar] [CrossRef]
  117. Kiko, T.; Nakagawa, K.; Tsuduki, T.; Furukawa, K.; Arai, H.; Miyazawa, T. MicroRNAs in Plasma and Cerebrospinal Fluid as Potential Markers for Alzheimer’s Disease. J. Alzheimer’s Dis. 2014, 39, 253–259. [Google Scholar] [CrossRef]
  118. Nagaraj, S.; Laskowska-Kaszub, K.; Debski, K.J.; Wojsiat, J.; Dabrowski, M.; Gabryelewicz, T.; Kuznicki, J.; Wojda, U. Profile of 6 MicroRNA in Blood Plasma Distinguish Early Stage Alzheimer’s Disease Patients from Non-Demented Subjects. Oncotarget 2017, 8, 16122–16143. [Google Scholar] [CrossRef]
  119. Nagaraj, S.; Want, A.; Laskowska-Kaszub, K.; Fesiuk, A.; Vaz, S.; Logarinho, E.; Wojda, U. Candidate Alzheimer’s Disease Biomarker Mir-483-5p Lowers Tau Phosphorylation by Direct Erk1/2 Repression. Int. J. Mol. Sci. 2021, 22, 3653. [Google Scholar] [CrossRef]
  120. Yang, T.T.; Liu, C.G.; Gao, S.C.; Zhang, Y.; Wang, P.C. The Serum Exosome Derived MicroRNA−135a, −193b, and −384 Were Potential Alzheimer’s Disease Biomarkers. Biomed. Environ. Sci. 2018, 31, 87–96. [Google Scholar] [CrossRef]
  121. Sun, X.; Wu, Y.; Gu, M.; Zhang, Y. MiR-342-5p Decreases Ankyrin G Levels in Alzheimer’s Disease Transgenic Mouse Models. Cell Rep. 2014, 6, 264–270. [Google Scholar] [CrossRef]
  122. Zhang, R.; Zhang, Q.; Niu, J.; Lu, K.; Xie, B.; Cui, D.; Xu, S. Screening of MicroRNAs Associated with Alzheimer’s Disease Using Oxidative Stress Cell Model and Different Strains of Senescence Accelerated Mice. J. Neurol. Sci. 2014, 338, 57–64. [Google Scholar] [CrossRef] [PubMed]
  123. Wang, L.; Liu, J.; Wang, Q.; Jiang, H.; Zeng, L.; Li, Z.; Liu, R. MicroRNA-200a-3p Mediates Neuroprotection in Alzheimer-Related Deficits and Attenuates Amyloid-Beta Overproduction and Tau Hyperphosphorylation via Coregulating BACE1 and PRKACB. Front. Pharmacol. 2019, 10, 806. [Google Scholar] [CrossRef] [PubMed]
  124. Gong, X.; Huang, M.; Chen, L. Mechanism of MiR-132-3p Promoting Neuroinflammation and Dopaminergic Neurodegeneration in Parkinson’s Disease. eNeuro 2022, 9, ENEURO.0393-21.2021. [Google Scholar] [CrossRef]
  125. Junn, E.; Lee, K.W.; Jeong, B.S.; Chan, T.W.; Im, J.Y.; Mouradian, M.M. Repression of alpha-synuclein expression and toxicity by microRNA-7. Proc. Natl. Acad. Sci. USA 2009, 106, 13052–13057. [Google Scholar] [CrossRef]
  126. Kabaria, S.; Choi, D.C.; Chaudhuri, A.D.; Mouradian, M.M.; Junn, E. Inhibition of miR-34b and miR-34c enhances alpha-synuclein expression in Parkinson’s disease. FEBS Lett. 2015, 589, 319–325. [Google Scholar] [CrossRef]
  127. O’Hara, D.M.; Pawar, G.; Kalia, S.K.; Kalia, L.V. LRRK2 and alpha-Synuclein: Distinct or Synergistic Players in Parkinson’s Disease? Front Neurosci. 2020, 14, 577. [Google Scholar] [CrossRef] [PubMed]
  128. Recasens, A.; Perier, C.; Sue, C.M. Role of MicroRNAs in the Regulation of α-Synuclein Expression: A Systematic Review. Front. Mol. Neurosci. 2016, 9, 128. [Google Scholar] [CrossRef]
  129. Zhao, N.; Wang, R.; Zhou, L.; Zhu, Y.; Gong, J.; Zhuang, S.M. MicroRNA-26b Suppresses the NF-ΚB Signaling and Enhances the Chemosensitivity of Hepatocellular Carcinoma Cells by Targeting TAK1 and TAB3. Mol. Cancer 2014, 13, 35. [Google Scholar] [CrossRef]
  130. Steiner, D.F.; Thomas, M.F.; Hu, J.K.; Yang, Z.; Babiarz, J.E.; Allen, C.D.C.; Matloubian, M.; Blelloch, R.; Ansel, K.M. MicroRNA-29 Regulates T-Box Transcription Factors and Interferon-γ Production in Helper T Cells. Immunity 2011, 35, 169–181. [Google Scholar] [CrossRef]
  131. Kole, A.J.; Swahari, V.; Hammond, S.M.; Deshmukh, M. MiR-29b Is Activated during Neuronal Maturation and Targets BH3-Only Genes to Restrict Apoptosis. Genes Dev. 2011, 25, 125–130. [Google Scholar] [CrossRef]
  132. Li, D.; Yang, H.; Ma, J.; Luo, S.; Chen, S.; Gu, Q. MicroRNA-30e Regulates Neuroinflammation in MPTP Model of Parkinson’s Disease by Targeting Nlrp3. Hum. Cell 2018, 31, 106–115. [Google Scholar] [CrossRef] [PubMed]
  133. Vistbakka, J.; Sumelahti, M.L.; Lehtimäki, T.; Elovaara, I.; Hagman, S. Evaluation of Serum MiR-191-5p, MiR-24-3p, MiR-128-3p, and MiR-376c-3 in Multiple Sclerosis Patients. Acta Neurol. Scand. 2018, 138, 130–136. [Google Scholar] [CrossRef] [PubMed]
  134. Zanoni, M.; Orlandi, E.; Rossetti, G.; Turatti, M.; Calabrese, M.; Gomez Lira, M.; Gajofatto, A. Upregulated Serum MiR-128-3p in Progressive and Relapse-Free Multiple Sclerosis Patients. Acta Neurol. Scand. 2020, 142, 511–516. [Google Scholar] [CrossRef]
  135. Li, C.; Wei, Q.; Gu, X.; Chen, Y.; Chen, X.; Cao, B.; Ou, R.; Shang, H. Decreased Glycogenolysis by MiR-338-3p Promotes Regional Glycogen Accumulation within the Spinal Cord of Amyotrophic Lateral Sclerosis Mice. Front. Mol. Neurosci. 2019, 12, 114. [Google Scholar] [CrossRef] [PubMed]
  136. Ricci, C.; Marzocchi, C.; Battistini, S. MicroRNAs as Biomarkers in Amyotrophic Lateral Sclerosis. Cells 2018, 7, 219. [Google Scholar] [CrossRef] [PubMed]
  137. Ibrahim, S.H.; El-Mehdawy, K.M.; Seleem, M.; El-Sawalhi, M.M.; Shaheen, A.A. Serum ROCK2, MiR-300 and MiR-450b-5p Levels in Two Different Clinical Phenotypes of Multiple Sclerosis: Relation to Patient Disability and Disease Progression. J. Neuroimmunol. 2020, 347, 577356. [Google Scholar] [CrossRef] [PubMed]
  138. Je, G.; Kim, Y.S. Mitochondrial ROS-Mediated Post-Transcriptional Regulation of α-Synuclein through MiR-7 and MiR-153. Neurosci. Lett. 2017, 661, 132–136. [Google Scholar] [CrossRef]
  139. Díez-Planelles, C.; Sánchez-Lozano, P.; Crespo, M.C.; Gil-Zamorano, J.; Ribacoba, R.; González, N.; Suárez, E.; Martínez-Descals, A.; Martínez-Camblor, P.; Álvarez, V.; et al. Circulating MicroRNAs in Huntington’s Disease: Emerging Mediators in Metabolic Impairment. Pharmacol. Res. 2016, 108, 102–110. [Google Scholar] [CrossRef]
  140. Langfelder, P.; Gao, F.; Wang, N.; Howland, D.; Kwak, S.; Vogt, T.F.; Aaronson, J.S.; Rosinski, J.; Coppola, G.; Horvath, S.; et al. MicroRNA Signatures of Endogenous Huntingtin CAG Repeat Expansion in Mice. PLoS ONE 2018, 13, e0190550. [Google Scholar] [CrossRef]
  141. Grossi, I.; Radeghieri, A.; Paolini, L.; Porrini, V.; Pilotto, A.; Padovani, A.; Marengoni, A.; Barbon, A.; Bellucci, A.; Pizzi, M.; et al. MicroRNA-34a-5p Expression in the Plasma and in Its Extracellular Vesicle Fractions in Subjects with Parkinson’s Disease: An Exploratory Study. Int. J. Mol. Med. 2021, 47, 533–546. [Google Scholar] [CrossRef]
  142. Tanaka, Y.; Tsuda, S.; Kunikata, H.; Sato, J.; Kokubun, T.; Yasuda, M.; Nishiguchi, K.M.; Inada, T.; Nakazawa, T. Profiles of Extracellular MiRNAs in the Aqueous Humor of Glaucoma Patients Assessed with a Microarray System. Sci. Rep. 2014, 4, 5089. [Google Scholar] [CrossRef] [PubMed]
  143. Hindle, A.G.; Thoonen, R.; Jasien, J.V.; Grange, R.M.H.; Amin, K.; Wise, J.; Ozaki, M.; Ritch, R.; Malhotra, R.; Buys, E.S. Identification of Candidate MiRNA Biomarkers for Glaucoma. Investig. Ophthalmol. Vis. Sci. 2019, 60, 134–146. [Google Scholar] [CrossRef] [PubMed]
  144. Seong, H.; Cho, H.-K.; Kee, C.; Song, D.H.; Cho, M.-C.; Kang, S.S. Profiles of MicroRNA in Aqueous Humor of Normal Tension Glaucoma Patients Using RNA Sequencing. Sci. Rep. 2021, 11, 19024. [Google Scholar] [CrossRef] [PubMed]
  145. Jayaram, H.; Phillips, J.I.; Lozano, D.C.; Choe, T.E.; Cepurna, W.O.; Johnson, E.C.; Morrison, J.C.; Gattey, D.M.; Saugstad, J.A.; Keller, K.E. Comparison of MicroRNA Expression in Aqueous Humor of Normal and Primary Open-Angle Glaucoma Patients Using PCR Arrays: A Pilot Study. Investig. Ophthalmol. Vis. Sci. 2017, 58, 2884–2890. [Google Scholar] [CrossRef]
  146. Hubens, W.H.G.; Krauskopf, J.; Beckers, H.J.M.; Kleinjans, J.C.S.; Webers, C.A.B.; Gorgels, T.G.M.F. Small RNA Sequencing of Aqueous Humor and Plasma in Patients with Primary Open-Angle Glaucoma. Investig. Ophthalmol. Vis. Sci. 2021, 62, 24. [Google Scholar] [CrossRef]
  147. Drewry, M.D.; Challa, P.; Kuchtey, J.G.; Navarro, I.; Helwa, I.; Hu, Y.; Mu, H.; Stamer, W.D.; Kuchtey, R.W.; Liu, Y. Differentially Expressed MicroRNAs in the Aqueous Humor of Patients with Exfoliation Glaucoma or Primary Open-Angle Glaucoma. Hum. Mol. Genet. 2018, 27, 1263–1275. [Google Scholar] [CrossRef]
  148. Sun, P.; Liu, D.Z.; Jickling, G.C.; Sharp, F.R.; Yin, K.J. MicroRNA-Based Therapeutics in Central Nervous System Injuries. J. Cereb. Blood Flow Metab. 2018, 38, 1125–1148. [Google Scholar] [CrossRef]
  149. Winkle, M.; El-Daly, S.M.; Fabbri, M.; Calin, G.A. Noncoding RNA Therapeutics—Challenges and Potential Solutions. Nat. Rev. Drug Discov. 2021, 20, 629–651. [Google Scholar] [CrossRef]
  150. Hamzei Taj, S.; Kho, W.; Riou, A.; Wiedermann, D.; Hoehn, M. MiRNA-124 Induces Neuroprotection and Functional Improvement after Focal Cerebral Ischemia. Biomaterials 2016, 91, 151–165. [Google Scholar] [CrossRef]
  151. Calin, G.A.; Cimmino, A.; Fabbri, M.; Ferracin, M.; Wojcik, S.E.; Shimizu, M.; Taccioli, C.; Zanesi, N.; Garzon, R.; Aqeilan, R.I.; et al. MiR-15a and MiR-16-1 Cluster Functions in Human Leukemia. Proc. Natl. Acad. Sci. USA 2008, 105, 5166–5171. [Google Scholar] [CrossRef]
  152. Cimmino, A.; Calin, G.A.; Fabbri, M.; Iorio, M.V.; Ferracin, M.; Shimizu, M.; Wojcik, S.E.; Aqeilan, R.I.; Zupo, S.; Dono, M.; et al. MiR-15 and MiR-16 Induce Apoptosis by Targeting BCL2. Proc. Natl. Acad. Sci. USA 2005, 102, 13944–13949. [Google Scholar] [CrossRef]
  153. Van Rooij, E.; Purcell, A.L.; Levin, A.A. Developing MicroRNA Therapeutics. Circ. Res. 2012, 110, 496–507. [Google Scholar] [CrossRef]
  154. Obad, S.; Dos Santos, C.O.; Petri, A.; Heidenblad, M.; Broom, O.; Ruse, C.; Fu, C.; Lindow, M.; Stenvang, J.; Straarup, E.M.; et al. Silencing of MicroRNA Families by Seed-Targeting Tiny LNAs. Nat. Genet. 2011, 43, 371–378. [Google Scholar] [CrossRef] [PubMed]
  155. Van Roosbroeck, K.; Fanini, F.; Setoyama, T.; Ivan, C.; Rodriguez-Aguayo, C.; Fuentes-Mattei, E.; Xiao, L.; Vannini, I.; Redis, R.S.; D’Abundo, L.; et al. Combining Anti-MiR-155 with Chemotherapy for the Treatment of Lung Cancers. Clin. Cancer Res. 2017, 23, 2891–2904. [Google Scholar] [CrossRef] [PubMed]
  156. Cortez, M.A.; Valdecanas, D.; Niknam, S.; Peltier, H.J.; Diao, L.; Giri, U.; Komaki, R.; Calin, G.A.; Gomez, D.R.; Chang, J.Y.; et al. In Vivo Delivery of MiR-34a Sensitizes Lung Tumors to Radiation through RAD51 Regulation. Mol. Ther. Nucleic Acids 2015, 4, e270. [Google Scholar] [CrossRef] [PubMed]
  157. Vo, D.D.; Staedel, C.; Zehnacker, L.; Benhida, R.; Darfeuille, F.; Duca, M. Targeting the Production of Oncogenic MicroRNAs with Multimodal Synthetic Small Molecules. ACS Chem. Biol. 2014, 9, 711–721. [Google Scholar] [CrossRef]
Figure 1. Similar neurodegenerative mechanisms between glaucoma and other neurodegenerative diseases. It has been described that several mechanisms are common between the above-mentioned neurodegenerative diseases, such as neuroinflammatory mechanisms that are associated with a major activation of astrocytes and microglia and/or by circulating immune cell infiltration due to neurovascular impairments. Genetic factors could also be implied to a minor degree and with differences between the diseases, but with some common mechanisms. Mitochondrial dysfunction and impaired energy metabolism along with increased ROS production are also major common mechanisms the lead to neuronal loss. Also, we observed that the presence of protein aggregates (e.g., Aβ oligomers, α-synuclein and hyperphosphorylated tau protein) can be a common feature of AD, PD and glaucoma. All these factors are aggravated with aging and in most cases are accompanied by other disturbs. Figure made using BioRender (Toronto, ON, Canada).
Figure 1. Similar neurodegenerative mechanisms between glaucoma and other neurodegenerative diseases. It has been described that several mechanisms are common between the above-mentioned neurodegenerative diseases, such as neuroinflammatory mechanisms that are associated with a major activation of astrocytes and microglia and/or by circulating immune cell infiltration due to neurovascular impairments. Genetic factors could also be implied to a minor degree and with differences between the diseases, but with some common mechanisms. Mitochondrial dysfunction and impaired energy metabolism along with increased ROS production are also major common mechanisms the lead to neuronal loss. Also, we observed that the presence of protein aggregates (e.g., Aβ oligomers, α-synuclein and hyperphosphorylated tau protein) can be a common feature of AD, PD and glaucoma. All these factors are aggravated with aging and in most cases are accompanied by other disturbs. Figure made using BioRender (Toronto, ON, Canada).
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Figure 2. Venn’s diagram showing the overlapping miRNAs described for the different neurodegenerative diseases. The diagram demonstrates an overlapping of some miRNAs between the above-mentioned diseases. AD and PD share miR-34b/c, miR-29b, miR-146a and miR-155; PD and ALS share miR-27a, miR-155, miR-142-5p, miR-450-5p, miR-34a miR-128-3p and miR-24-3p; PD and HD share miR-34a-5p; AD, PD and HD share miR-124 and miR-132; AD and glaucoma (GL) share miR-29, miR124 and 125b. There is no overlapping of the neuroprotective miRNAs described in the present review that might share a similar biological function in all the discussed neurodegenerative diseases.
Figure 2. Venn’s diagram showing the overlapping miRNAs described for the different neurodegenerative diseases. The diagram demonstrates an overlapping of some miRNAs between the above-mentioned diseases. AD and PD share miR-34b/c, miR-29b, miR-146a and miR-155; PD and ALS share miR-27a, miR-155, miR-142-5p, miR-450-5p, miR-34a miR-128-3p and miR-24-3p; PD and HD share miR-34a-5p; AD, PD and HD share miR-124 and miR-132; AD and glaucoma (GL) share miR-29, miR124 and 125b. There is no overlapping of the neuroprotective miRNAs described in the present review that might share a similar biological function in all the discussed neurodegenerative diseases.
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Vasconcelos, C.F.M.; Ribas, V.T.; Petrs-Silva, H. Shared Molecular Pathways in Glaucoma and Other Neurodegenerative Diseases: Insights from RNA-Seq Analysis and miRNA Regulation for Promising Therapeutic Avenues. Cells 2023, 12, 2155. https://doi.org/10.3390/cells12172155

AMA Style

Vasconcelos CFM, Ribas VT, Petrs-Silva H. Shared Molecular Pathways in Glaucoma and Other Neurodegenerative Diseases: Insights from RNA-Seq Analysis and miRNA Regulation for Promising Therapeutic Avenues. Cells. 2023; 12(17):2155. https://doi.org/10.3390/cells12172155

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Vasconcelos, Carlos Franciney Moreira, Vinicius Toledo Ribas, and Hilda Petrs-Silva. 2023. "Shared Molecular Pathways in Glaucoma and Other Neurodegenerative Diseases: Insights from RNA-Seq Analysis and miRNA Regulation for Promising Therapeutic Avenues" Cells 12, no. 17: 2155. https://doi.org/10.3390/cells12172155

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