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Article

piRNA and miRNA Can Suppress the Expression of Multiple Sclerosis Candidate Genes

by
Saltanat Kamenova
1,
Aksholpan Sharapkhanova
2,
Aigul Akimniyazova
1,
Karlygash Kuzhybayeva
1,
Aida Kondybayeva
1,
Aizhan Rakhmetullina
3,4,
Anna Pyrkova
1,5 and
Anatoliy Ivashchenko
5,*
1
Higher School of Medicine, Faculty of Medicine and Healthcare, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan
2
Department of Nervous Diseases, Asfendiyarov Kazakh National Medical University, Almaty 050012, Kazakhstan
3
Department of Technology of Production of Livestock Products, A. Baitursynov Kostanay Regional University, Kostanay 110000, Kazakhstan
4
Institute of Biochemistry and Biophysics, Polish Academy of Sciences, 02-106 Warsaw, Poland
5
Center for Bioinformatics and Nanomedicine, Almaty 050060, Kazakhstan
*
Author to whom correspondence should be addressed.
Nanomaterials 2023, 13(1), 22; https://doi.org/10.3390/nano13010022
Submission received: 29 October 2022 / Revised: 2 December 2022 / Accepted: 17 December 2022 / Published: 21 December 2022
(This article belongs to the Section Biology and Medicines)
Browse Figures
Versions Notes

Abstract

:
Multiple sclerosis (MS) is a common inflammatory demyelinating disease with a high mortality rate. MS is caused by many candidate genes whose specific involvement has yet to be established. The aim of our study was to identify endogenous miRNAs and piRNAs involved in the regulation of MS candidate gene expression using bioinformatic methods. A program was used to quantify the interaction of miRNA and piRNA nucleotides with mRNA of the target genes. We used 7310 miRNAs from three databases and 40,000 piRNAs. The mRNAs of the candidate genes revealed miRNA binding sites (BSs), which were located separately or formed clusters of BSs with overlapping nucleotide sequences. The miRNAs from the studied databases were generally bound to mRNAs in different combinations, but miRNAs from only one database were bound to the mRNAs of some genes. For the first time, a direct interaction between the complete sequence of piRNA nucleotides and the nucleotides of their mRNA BSs of target genes was shown. One to several clusters of BSs of miRNA and piRNA were identified in the mRNA of ADAM17, AHI1, CD226, EOMES, EVI5, IL12B, IL2RA, KIF21B, MGAT5, MLANA, SOX8, TNFRSF1A, and ZBTB46 MS candidate genes. These piRNAs form the expression regulation system of the MS candidate genes to coordinate the synthesis of their proteins. Based on these findings, associations of miRNAs, piRNAs, and candidate genes for MS diagnosis are recommended.

1. Introduction

The miRNAs (mRNA-inhibitory RNAs) regulate gene expression at the post-transcriptional level and play an important role in many cellular processes. Many genes whose expression depends on miRNA cause diseases, including multiple sclerosis (MS) [1,2,3,4,5]. A number of studies have suggested that multiple sclerosis can be diagnosed by using marker miRNAs as correlations between changes in miRNA concentrations and the disease have been found [6,7,8,9,10,11]. In recent years, the influence of small miRNAs on the development of MS has been actively studied [12,13,14,15,16,17]. Additionally, various methods of multiple sclerosis therapy involving the use of miRNAs have been proposed [18,19,20,21,22,23,24].
Considering that the number of candidate genes is in the several tens, and the number of miRNA is more than seven thousand, it is very difficult and expensive to determine in wet experiments which associations of miRNAs and target genes can be markers of MS. The use of computational technologies accelerates this task by a factor of thousands and significantly reduces the material costs of finding effective miRNA and candidate target gene associations. In general, elucidating the possible influence of miRNA on the expression of MS candidate genes is necessary, as there is only limited information on their relationships. We have used bioinformatic approaches to establish quantitative characteristics of the interaction between the miRNAs and mRNAs of candidate genes of various diseases, which have allowed us to identify how miRNAs are associated with candidate MS genes.
Note that most studies study only a few miRNAs whose expression correlates with MS disease and do not identify specific candidate target genes. For these reasons, there are many uncertainties in identifying effective associations between small miRNAs and candidate genes. This approach cannot adequately identify significant associations between miRNAs and candidate target genes, and from them, cannot select the most effective associations. One of the problems with elucidating the involvement of miRNAs in various diseases is the common misconception that miRNAs cause disease without the involvement of candidate genes. For example, correlations between changes in miRNA concentration and the development of disease are being established. In fact, miRNAs cause pathologies through their target genes. Moreover, some miRNAs regulate the expression of several or even hundreds of genes, and the expression of one gene depends on dozens of miRNAs [25]. Therefore, the common approach of detecting changes in a few miRNAs out of several thousand miRNAs through pathology without identifying their target genes out of more than 20 thousand human genes is highly inefficient. In this situation, only preliminary bioinformatic studies of the possible interactions between all miRNAs and mRNA candidate genes, as well as all human genes, can significantly and more objectively establish associations between the miRNAs and candidate genes involved in a particular disease. In the present study, we investigated the effects of miRNAs from the miRBase database (http://www.mirbase.org/ (accessed on 1 April 2022), and miRNAs from the studies of Londin et al. [26] and Backes et al. [27], to significantly increase the probability of detecting miRNAs involved in the development of MS.
We studied the possible effects of piRNA (PIWI-binding RNA) molecules on MS candidate genes. miRNAs are 20–25 nt and 5–9 nanometers long, whereas piRNAs are, on average, eight nucleotides longer than miRNAs (25–34 nt) [28] and, therefore, are nanoscale biological structures. The piRNAs can bind more strongly to the mRNA. Unfortunately, notions about the biological role of piRNAs have remained insufficiently substantiated over the many years since their discovery [29]. Some publications suggest that piRNAs can participate in the development of neurodegenerative diseases, but how this happens is unknown [30]. We hypothesized that piRNAs can bind to mRNAs and, as with miRNAs, suppress protein synthesis [31]. We tested this assumption in a recent study using the example of candidate genes involved in the development of multiple sclerosis. However, the putative mechanisms of this piRNA effect are highly questionable, and it is not even suggested that piRNA is involved in the regulation of candidate gene expression. At the same time, the interaction between piRNA and PIWI proteins involving the formation of complexes has been described. This suggests that the interaction between piRNAs and mRNAs is similar to the interaction between the RISC complexes of miRNAs and mRNAs. We are not aware of any attempt to determine the interaction between piRNAs and mRNAs by means of the known programs for miRNA–mRNA interaction determination, as the determination of the interaction between mRNAs and 26–35 nt piRNAs using the so-called “seed” programs is inadequate.

2. Materials and Methods

The nucleotide sequences of the RNAs candidate MS genes were downloaded from the NCBI website (http://www.ncbi.nlm.nih.gov (accessed on 1 April 2022)). The list of studied candidate MS genes is given in Table S1. The nucleotide sequences of the piRNAs were obtained from Wang et al. [28]. The 2567 miRNAs were taken from miRBase v.22 (http://www.mirbase.org (accessed on 1 April 2022)). The 3707 miRNAs were obtained from an article by Londin et al. [26], and the 1036 miRNA were obtained from an article by Backes et al. [27]. In order to establish the possible effect of miRNA and piRNAs on the MS candidate genes, we determined the interaction characteristics using the MirTarget program [32]. This program defines the following features of miRNA and piRNA binding to mRNA: (a) the initiation of the miRNA and piRNA binding to the mRNAs from the first nucleotide of the mRNAs; (b) the localization of the piRNA and miRNA BSs in the 5′UTR (5′-untranslated region), CDS (coding sequence), and 3′UTR (3′-untranslated region) of the mRNAs; (c) the schemes of nucleotide interactions between piRNAs, miRNAs, and mRNAs; (d) the free energy of the interaction between piRNA and the mRNA (ΔG, kJ/mol); (e) the ratio ΔG/ΔGm (%) is determined for each site (ΔGm equals the free energy of the piRNA binding with its fully complementary nucleotide sequence). The MirTarget program finds hydrogen bonds between adenine (A) and uracil (U), guanine (G) and cytosine (C), G and U, and A and C. Regarding the free energy of interactions (ΔG), a pair of G and C is equal to 6.37 kJ/mol, a pair of A and U is equal to 4.25 kJ/mol, and the pairs of G and U, and A and C are equal to 2.12 kJ/mol [33]. The distances between the bound A and C (1.04 nm), and G and U (1.02 nm) pairs are similar to those between the bound G and C, and A and U pairs, which are equal to 1.03 nm. The numbers of hydrogen bonds in the G–C, A–U, G–U, and A–C interactions were 3, 2, 1, and 1, respectively [34,35,36]. Consideration of the schemes shows which nucleotides of non-canonical pairs increase the energy of interaction between piRNAs and BSs.
The MirTarget program has proven itself useful in the search for associations of miRNAs and target genes in various diseases [37,38,39,40,41,42]. This program makes it possible to determine the quantitative characteristics of these miRNAs with mRNAs, which is very difficult to establish in wet experiments. Due to these characteristics, it is possible to assess the competition among miRNAs and piRNAs for binding to candidate target genes. The adequacy of the program in terms of finding BSs has been confirmed in several publications. A better confirmation of the obtained results as compared to “wet” experiments is provided by the schemes of interaction of nucleotides along the entire length of the miRNAs, piRNAs, and BSs. The schemes can be verified manually by finding the predicted piRNA BSs in the mRNA nucleotide sequence in the NCBI database.

3. Results

A list of MS target genes for miRNAs and piRNAs that indicates previous publications of the participation of the candidate genes in the development of MS is given in Table S1. Of these, CD86, CD226, CLEC16A9, CYP27B1, FOXP3, IL2RA, IL-22RA2, IQGAP1, and MERTK were targets for miRNAs, and FCRL3, HLA-DRB1, MAPK1, MLANA, MYC, TALDO1, and TRIP11 genes were targets for piRNAs. The ADAM17, AHI1, CD6, EOMES, EVI5, IL12B, KIF21B, MGAT5, SOX8, TAGAP, TBX21, TNFRSF1A, ZBTB46, and ZMIZ1 genes were targets for piRNA and miRNA.

3.1. miRNA Interactions with 5′UTR mRNA of MS Genes

The data presented in Table 1 indicate the presence of BSs for miRNAs with the 5’UTR of mRNAs of several candidate genes. Only single BSs were detected in the remaining mRNAs of six genes (Table S2). A specific feature of some MS candidate genes is the interaction of miRNAs groups with the mRNAs of two or more genes. For example, the 5’UTR mRNA of the EVI5 gene contains a cluster of BSs of nine miRNAs that are 42 nt long. The sum of the lengths of these miRNAs BSs is 206 nt, which is 4.9 times the length of the cluster. This BS compaction results in a length-saving 5’UTR. However, we believe that the main purpose of the BSs compaction is to create competition between the miRNAs when binding to the mRNAs of the target gene to control its expression, resulting in more miRNAs binding with more free energy. Note that ID01702.3p-miR has three BSs, giving it an advantage in regulating EVI5 gene expression. The mRNA of the KIF21B gene contains a 72 nt BSs cluster for nine miRNAs (Table 1), and thus, there is less competition between miRNAs because two miRNAs can bind in the cluster, such as ID03151.3p-miR and ID00049.5p-miR. The SOX8 gene is less dependent on miRNAs because it has BSs in the 5’UTR mRNA for only four miRNAs. The EVI5 and KIF21B genes have BSs for ID00296.3p-miR, ID01641.3p-miR, and ID01702.3p-miR in clusters. This indicates a relationship between the expression regulations of these genes. If the mRNA synthesis of the EVI5 gene increases in the cell, these three miRNAs will bind to it more strongly, resulting in their lower inhibitory effect on the KIF21B gene mRNA. For MS diagnosis, the interaction between the free energy of miRNAs and the mRNA of the EVI5 and KIF21B genes with a ΔG value of more than −130 kJ/mol is recommended. When selecting miRNA and target gene associations for diagnosis, the concentration of miRNAs should also be considered, as a high concentration of medium-interacting miRNAs can ultimately have a decisive effect on gene expression.

3.2. miRNA Interactions with CDS mRNA of MS Genes

The results of the miRNA interaction with CDS mRNA of MS candidate genes are provided in Table 2 and Table S3. A large BSs cluster was revealed in the CDS mRNA of candidate MS genes only for the EOMES gene (Table 2). For some miRNAs, the cluster had two to six BSs. Taking into account multiple BSs, the sum of the miRNAs lengths compared to the cluster length was 16.5 times greater. Associations between the EOMES gene and ID01702.3p-miR, ID02294.5p-miR, ID00296.3p-miR, ID01804.3p-miR, ID01041.5p-miR, ID01106.5p-miR, and ID02064.5p-miR are recommended for a diagnosis of disease. Note that the CDS mRNAs of the EOMES gene contains BSs for ID01702.3p-miR, ID00296.3p-miR, and ID02294.5p-miR, which bind to the 5’UTR of the mRNAs of the EVI5 gene. In addition, the CDS mRNAs of the EOMES gene contained BSs for ID01702.3p-miR, ID00296.3p-miR, and ID00061.3p-miR, which bind to the BSs of the mRNAs cluster of the KIF21B gene. It was noted above that the EVI5 and KIF21B genes have BSs for ID00296.3p-miR, ID02294.5p-miR, and ID01702.3p-miR in clusters, indicating a relationship between the regulation of expression of these genes (Table 1). The EOMES gene can also be added, whose expression depends on ID01702.3p-miR and ID00296.3p-miR. The BSs of the mRNA cluster of the TNFRSF1A gene contains predominantly miRNAs from the Backes database. This cluster has a high degree of compaction, as its length is 6.6 times shorter than the sum of the BSs of miRNAs.

3.3. miRNA Interactions with 3′UTR mRNA of MS Genes

The role of miRNAs from the Backes database (b-miRNA), which had many targets in the 3’UTR mRNA of the candidate MS genes, is surprising. Several MS candidate genes contained clusters of b-miRNA BSs in their mRNAs (Table 3 and Table S4). The 3’UTR mRNA of ADAM17 contained a 33 nt long seven-miRNAs BSs cluster. Of these miRNAs, b-miR-1367-5p, b-miR-531-5p, b-miR-1641-5p, and b-miR-2038-5p had BSs in the mRNAs of the AHI1 gene cluster (blue color). The mRNAs of the ADAM17 and AHI1 genes could complementarily bind with miR-619-5p and miR-5096, respectively.
The mRNA of the CD226 gene contained four BSs clusters of predominantly b-miRNA (Table 3). The first BSs cluster from 5739 nt to 5763 nt could bind b-miR-1752-3p, b-miR-1441-3p, b-miR-1449-3p, b-miR-1189-3p, b-miR-1169-3p (completely complementary), miR-1273g-3p, and b-miR-2289-3p, and which could also bind to the fourth BSs cluster from 8642 nt to 8666 nt. This duplication of the BSs group of miRNAs indicates the importance of CD226 gene expression control by the identified miRNAs. In addition, b-miR-2083-3p and miR-5585-3p, as well as miR-1273g-3p and b-miR-2289-3p, each have two remote BSs. The b-miR-2038-3p has BSs in the mRNA of the ADAM17, AHI1, and CD226 genes, and b-miR-2038-3p binds fully complementarily in the mRNA of the CD226 gene.
The next target gene for many miRNAs was EVI5, in whose mRNA four large clusters of BSs, predominantly b-miRNAs, were identified (Table 3). The first cluster contained BSs for b-miR-1035-3p, b-miR-1752-3p, b-miR-1441-3p, b-miR-2164-3p, b-miR-1169-3p, b-miR-1189-3p, miR-1273g-3p, and b-miR-2289-3p, which bind in the CD226 gene mRNA cluster. Some of these miRNAs (b-miR-1441-3p, b-miR-2164-3p, b-miR-1169-3p, b-miR-1189-3p, miR-1273g-3p, and b-miR-2289-3p) bind in the second cluster from 5498 nt to 5521 nt, and miR-1273g-3p binds completely complementarily.
In the last mRNA BSs cluster of the EVI5 gene from 7373 nt to 7396 nt, the BSs of ID01836.5p-miR, b-miR-1367-5p, b-miR-1361-5p, b-miR-531-5p, b-miR-1131-5p, b-miR-1641-5p, b-miR-2038-5p, and b-miR-1608-5p were revealed. The BSs of these miRNAs were located in the mRNA of the IL2RA gene in an identical sequence (Table 3), and b-miR-531-5p, b-miR-1641-5p, b-miR-2038-5p, and b-miR-1608-5p bind completely complementarily.
The interaction of ID00436.3p-miR, ID01030.3p-miR, and miR-466 with the MGAT5 gene mRNA is noteworthy (Table 3). The beginning of the BSs of these miRNAs are located two nucleotides apart and the BSs are repeated 13–14 times. Such multiple BSs greatly increase the binding probability of each miRNA, which increases the dependence of MGAT5 gene expression on these miRNAs. In addition to a set of miRNAs organized into groups according to the principle of binding in clusters, miRNAs having one BS in the mRNA of the target gene can have a significant effect on the expression of candidate MS genes. Their effect depends on the ratio of the concentrations of miRNAs and mRNAs of the target gene. Quite often, readers have doubts about the reliability of the prediction of the BSs of miRNAs with mRNAs of target genes. To confirm the quantitative characteristics of the interaction between miRNAs and the mRNAs of target genes, we present schemes for the formation of hydrogen bonds between interacting canonical and non-canonical nucleotide pairs (Figure 1).
The given schemes convincingly demonstrate the interaction of nucleotides, and the values of the free energy of interaction are given. These characteristics were obtained based on semi-empirical values of the interaction of nucleotides in an aqueous medium due to hydrogen bonds, and in a comparative aspect, they reflect well these interactions of nucleotides between miRNA and mRNA target genes.

3.4. piRNA Interactions with 5′UTR mRNA of MS Genes

piRNAs can bind to the 5’UTRs of the mRNAs of candidate MC genes with a free energy higher than that of miRNAs (Table S5). When the piRNAs interact with mRNA candidate target genes, the free energy ranges from −140 kJ/mol to −159 kJ/mol. The mRNA of TAGAP, TALDO1, and TBX21 genes each had a single BS. Expression of the MYC gene can be regulated by three piRNAs, and the ZBTB46 gene by six piRNAs. The BSs of the four piRNAs formed a cluster from 37 nt to 96 nt in the mRNA of the ZBTB46 gene, resulting in competition between these piRNAs. TNFRSF1A gene expression could depend on eight piRNAs whose BSs formed a cluster 56 nt long, which was 6.4 times shorter than the sum of the piRNA lengths. Despite the considerable length of piRNA (27–34 nt), the interaction between the piRNA and mRNA nucleotides occurs along the entire length of the piRNA, which is clearly seen in Figure 2.

3.5. piRNA Interactions with CDS mRNA of MS Genes

The results of the piRNA effects on CDS mRNA candidate MS genes are shown in Table S6. Eight genes were targeted by one piRNA each. The mRNA of the HLA-DRB1 gene contained two BSs, which were arranged with overlapping nucleotide sequences. The mRNAs of the CD6 and KIF21B genes each contained three BSs located throughout the coding sequence. The mRNA of the EOMES gene could bind to five piRNAs, with the BSs of piR-5938, piR-5937, and piR-1344 forming a BSs cluster only 34 nt long. That is, the competition between these piRNAs for binding to mRNA was high.
The TNFRSF1A gene was targeted by 17 piRNAs whose BSs occupied a nearly continuous stretch of 102 nt. Given that the cluster of piRNA BSs in the CDS of the mRNA of the TNFRSF1A gene begins at 874 nt (Table 4) and continues into the CDS from 976 nt, the cluster of BSs is actually continuous and located in the CDS. This is the first time we have detected such a phenomenon. Enhanced control of TNFRSF1A gene expression by piRNA is apparently associated with its high risk of involvement in neurodegenerative diseases including multiple sclerosis.

3.6. piRNA Interactions with 3′UTR mRNA of MS Genes

The mRNAs of the ADAM17 and AHI1 genes have BSs for piR-16315, piR-5295, piR-5300, piR-5301, piR-5303, piR-5294, piR-6236, piR-7637, and piR-5358, which are part of mRNAs BSs clusters of both genes (Table 5).
Consequently, these piRNAs will compete to regulate the expression of both genes. The ADAM17 and AHI1 genes have different functions but are regulated by several of the same piRNAs involving other piRNAs. Hence, the regulation of the expression of these genes by such a set of piRNAs is necessary for the coordinated expression of these genes.
The cluster of nine piRNA BSs from 3501 nt to 3538 nt in the mRNA of the ADAM17 gene is 37 nt long and is 7.3 times less than the sum of piRNA BSs. The mRNA of the AHI1 gene can bind the same nine piRNAs that bind in the BSs cluster located from 4455 nt to 4491 nt. The total length of the piRNA BSs in this cluster is 271 nt and is 7.3 times the length of the cluster. A cluster of piR-5744, piR-7102, and piR-7105 BSs in the 3′UTR mRNA of the AHI1 gene was detected in the 3′UTR mRNA of the EVI5 gene at positions of 6872 nt, 6877 nt, and 6878 nt and in the 3′UTR mRNA of the IL2RA gene at positions 2115 nt, 2120 nt, and 2121 nt.
The most abundant in BSs piRNA clusters was the mRNA of the EVI5 gene (Table 4). The first cluster of BSs consists of piR-10936, piR-10886, and piR-10885 BSs at positions 3378 nt, 3379 nt, and 3379 nt. These three piRNAs together with piR-1248, piR-10873, piR-11596, piR-10873, piR-10935, and piR-10934 form a BSs cluster from 5078 nt to 5108 nt in the 3′UTR mRNA of the EVI5 gene and in the 3′UTR mRNA of the MLANA gene in positions from 1005 nt to 1035 nt.
A second BSs cluster of piR-4815, piR-10936, piR-10886, and piR-10885 in the 3′UTR mRNA of the EVI5 gene was detected in the 5′UTR mRNA of the TNFRSF1A gene (Table 4). In the BSs cluster, piR-9994, piR-9059, piR-9036, piR3586, piR-7244, piR-5505, and piR-2344 bind from 3403 nt to 3475 nt. In the cluster of BSs from 5104 nt to 5139 nt, these same piRNAs can also bind in the 3′UTR mRNA of the EVI5 gene.
A BSs cluster for piR-1254, piR-12623, piR-4112, and piR-12340 was located from 3517 nt to 3573 nt, and an identical BSs cluster from 4829 nt to 4886 nt was found in the 3′UTR mRNA of the EVI5 gene. The BSs of piR-12346, piR-12399, piR-5505, piR-2344, piR-15405, piR-14239, and piR-15404 are located at a small interval after each of these clusters (Table 5).
The large BSs cluster for the ten piRNAs is located at 5215 nt in the 3′UTR mRNA of the EVI5 gene. The BSs of these same piR-1254, piR-12623, piR-17617, piR-11421, piR-12346, piR-12399, piR-15405, piR-14239, piR-15404, and piR-14625 BSs are located in the 3′UTR mRNA of the MLANA gene at 1142 nt.
Consequently, these piRNAs will compete with each other in two BSs clusters. The piR-12623 and piR-12399 BSs are located in the third mRNA site of the EVI5 gene, which is also located at 39 nt. The results obtained indicate an increased dependence in EVI5 gene expression on many piRNAs. The schemes of interaction between piRNA nucleotides and 3’UTR mRNA nucleotides of the candidate genes clearly show a strong interaction between piRNA and mRNA (Figure 2).
Not all MS candidate genes were targeted by piRNA. The results of the characterization of piRNA interaction with the mRNA of the eight MS candidate genes are shown in Table 4. The piRNA BSs were predominantly located in the 3’UTR and only two genes were located in the 5’UTR and the TNFRSF1A gene in the CDS. The mRNAs of the IL2RA, MGAT5, and ZBTB46 genes each had one piRNA BS, and the mRNA of the MLANA gene had three piRNA BSs. Each of the ADAM17, AHI1, EVI5, and TNFRSF1A genes was the target of several piRNAs whose BSs were located with overlapping nucleotides, which we called clusters of BSs. In the mRNA of the ADAM17 gene, the cluster was located from 3508 nt to 3539 nt and was 8.4 times shorter than the sum of the BSs of the nine piRNAs. This compactization of BSs leads to competition between piRNAs when binding to the mRNA of the target gene.
As evidenced by the above piRNA and target gene interactions, the expression of genes that are targets of one or more of the same or different piRNAs is regulated in a linked manner. For example, an increase in the expression of a particular gene leads to the binding of the corresponding piRNA, which, in turn, will suppress less the expression of its other target genes. The decreased expression of a target gene of any piRNA will result in the suppression of other target genes of that piRNA. Consequently, piRNAs that have a set of target genes maintain a balance in the expression of their target genes.
Another aspect of the effect of piRNA and miRNA on a single gene is that at the beginning of ontogenesis, the proportion of synthesized piRNAs is greater than the proportion of miRNAs [36]. During phylogenesis, the piRNA fraction decreases and the miRNA fraction increases in differentiated cells. Therefore, piRNA and miRNA target genes will be less dependent on piRNA and more dependent on miRNA. The piRNA and miRNA target genes were EOMES, ADAM17, AHI1, EVI5, IL2RA, and MGAT5. These genes were most dependent on piRNA and miRNA, and therefore, their associations with the corresponding piRNA and miRNA are the most suitable for use in MS diagnosis.

4. Discussion

This work shows that to search targeting MS candidate genes for miRNAs, all known databases must be searched and used. If this requirement is ignored, it will be difficult to obtain unbiased data on the effect of miRNAs on candidate genes, including those of MS. The inclusion of piRNAs affecting MS candidate genes in the search significantly expands our understanding of the causes of MS development. It is known that piRNAs are synthesized predominantly in the early stages of ontogenesis and subsequently their synthesis continues in stem cells [43]. In contrast, miRNAs are weakly expressed in the initial stages of embryogenesis and are synthesized in most organs as the body tissues differentiate [43]. Note that in the present work, we found that some genes are simultaneously targeted by piRNA and miRNA. The EOMES, ADAM17, AHI1, EVI5, IL2RA, and MGAT5 genes were targets for piRNA and miRNA (Supplementary Table S1). This reflects the different expressions of these genes at the initial stages of ontogenesis and during ontogenesis. These genes were most dependent on piRNA and miRNA, and therefore, their associations with the corresponding piRNA and miRNA are most suitable for use in MS diagnosis. If these genes are candidate disease genes, the likelihood of disease, particularly age-related disease, increases over the course of ontogeny. The identified interactions of several miRNAs and piRNAs in the mRNA of a gene, especially those interacting in clusters, necessitate monitoring the expression of candidate genes and piRNAs with miRNAs to reveal an objective assessment of the patient’s condition. The different interaction characteristics of different miRNAs and different piRNAs with the mRNA of the target gene require a comparison of their concentrations in combination with the expression of the target gene. As evidenced by the above piRNA and target gene interactions. The expression of genes that target one or more of the same or different piRNAs is regulated in a linked way. For example, an increase in the expression of a particular gene leads to the binding of the corresponding piRNA, which, in turn, will lead to less suppression of the expression of its other target genes. The decreased expression of a target gene of any piRNA will result in the suppression of other target genes of that piRNA. Consequently, piRNAs that have a set of target genes maintain a balance in the expression of their target genes. Another aspect of the effect of piRNA and miRNA on a single gene is that at the beginning of ontogenesis, the proportion of synthesized piRNAs is greater than the proportion of miRNAs. During phylogenesis, the piRNA fraction decreases, and the miRNA fraction increases in differentiated cells [43]. Therefore, piRNA and miRNA target genes will be less dependent on piRNA and more dependent on miRNA. The piRNAs and miRNAs have been shown to be key regulators of the expression of candidate MS genes. Establishing associations between piRNA, miRNA, and target MS candidate genes reveals how the expression of candidate genes depends on the concentrations of piRNA and miRNA. This will allow such associations to be used as markers of disease and for the development of subsequent therapies. Analysis of the interaction of miRNAs and piRNAs with the mRNA of MS candidate genes showed that there are groups of miRNAs and piRNAs that interact with the mRNA of one or two genes (these are colored in the tables). Measuring the concentration of such groups of miRNAs or piRNAs along with the expression of one or two candidate target genes provides a much better chance of determining which miRNAs or piRNAs regulate the expression of these genes and to what extent. Next, we need to determine which human genes are also targets of these miRNAs or piRNAs in order to establish what side-effects these miRNAs or piRNAs may have when used as diagnostic markers and as therapeutic agents.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nano13010022/s1, Table S1: List of MS target genes for miRNAs and piRNAs with an indication of the publication of the participation of the candidate gene in the development of MS; Table S2: Characteristics of miRNA interactions with 5′UTR mRNA of candidate MS genes; Table S3: Characteristics of miRNA interactions with CDS mRNA of candidate MS genes; Table S4: Characteristics of miRNA interactions with 3′UTR mRNA of candidate MS genes; Table S5: Characteristics of interaction between piRNA and 5’UTR mRNA of MS genes; Table S6: Characteristics of interaction between piRNA and CDS mRNA of MS genes.

Author Contributions

Conceptualization, A.I. and S.K.; methodology, A.P. and A.A.; software, A.P. and A.I.; validation, A.S., K.K. and A.R.; investigation, A.K.; resources, A.I.; data curation, A.K. and A.I.; writing—original draft preparation, A.I. and S.K.; writing—review and editing, A.I. and A.A.; visualization, A.A.; supervision, A.I.; funding acquisition, A.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Center for Bioinformatics and Nanomedicine.

Data Availability Statement

Data are contained within the present article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Tufekci, K.U.; Oner, M.G.; Genc, S.; Genc, K. MicroRNAs and Multiple Sclerosis. Autoimmune Dis. 2010, 2011, 807426. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Junker, A. Pathophysiology of translational regulation by microRNAs in multiple sclerosis. FEBS Lett. 2011, 585, 3738–3746. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Junker, A.; Hohlfeld, R.; Meinl, E. The emerging role of microRNAs in multiple sclerosis. Nat. Rev. Neurol. 2011, 7, 56–59. [Google Scholar] [CrossRef] [PubMed]
  4. de Faria, O., Jr.; Moore, C.S.; Kennedy, T.E.; Antel, J.P.; Ebar-Or, A.; Dhaunchak, A.S. MicroRNA dysregulation in multiple sclerosis. Front. Genet. 2013, 3, 311. [Google Scholar] [CrossRef] [Green Version]
  5. Zare-Shahabadi, A.; Renaudineau, Y.; Rezaei, N. MicroRNAs and multiple sclerosis: From physiopathology toward therapy. Expert Opin. Ther. Targets 2013, 17, 1497–1507. [Google Scholar] [CrossRef] [PubMed]
  6. Koch, M.W.; Metz, L.M.; Kovalchuk, O. Epigenetics and miRNAs in the diagnosis and treatment of multiple sclerosis. Trends Mol. Med. 2013, 19, 23–30. [Google Scholar] [CrossRef] [PubMed]
  7. Kacperska, M.; Walenczak, J.; Tomasik, B. Plasmatic microRNA as Potential Biomarkers of Multiple Sclerosis. Adv. Clin. Exp. Med. 2016, 25, 775–779. [Google Scholar] [CrossRef] [Green Version]
  8. Vistbakka, J.; Elovaara, I.; Lehtimäki, T.; Hagman, S. Circulating microRNAs as biomarkers in progressive multiple sclerosis. Mult. Scler. J. 2016, 23, 403–412. [Google Scholar] [CrossRef]
  9. Manna, I.; Iaccino, E.; Dattilo, V.; Barone, S.; Vecchio, E.; Mimmi, S.; Filippelli, E.; Demonte, G.; Polidoro, S.; Granata, A.; et al. Exosome-associated miRNA profile as a prognostic tool for therapy response monitoring in multiple sclerosis patients. FASEB J. 2018, 32, 4241–4246. [Google Scholar] [CrossRef] [Green Version]
  10. Duffy, C.P.; McCoy, C.E. The Role of MicroRNAs in Repair Processes in Multiple Sclerosis. Cells 2020, 9, 1711. [Google Scholar] [CrossRef]
  11. Peplow, P.V.; Martinez, B. MicroRNAs in blood and cerebrospinal fluid as diagnostic biomarkers of multiple sclerosis and to monitor disease progression. Neural Regen. Res. 2020, 15, 606–619. [Google Scholar] [CrossRef] [PubMed]
  12. Wang, L.; Zhang, L. MicroRNAs in amyotrophic lateral sclerosis: From pathogenetic involvement to diagnostic biomarker and therapeutic agent development. Neurol. Sci. 2020, 41, 3569–3577. [Google Scholar] [CrossRef] [PubMed]
  13. Ehtesham, N.; Mosallaei, M.; Karimzadeh, M.R.; Moradikazerouni, H.; Sharifi, M. microRNAs: Key modulators of disease-modifying therapies in multiple sclerosis. Int. Rev. Immunol. 2020, 39, 264–279. [Google Scholar] [CrossRef] [PubMed]
  14. Gholami, S.; Mirian, M.; Eftekhari, S.M.; Aliomrani, M. Apamin administration impact on miR-219 and miR-155-3p expression in cuprizone induced multiple sclerosis model. Mol. Biol. Rep. 2020, 47, 9013–9019. [Google Scholar] [CrossRef]
  15. Mycko, M.P.; Baranzini, S.E. microRNA and exosome profiling in multiple sclerosis. Mult. Scler. J. 2020, 26, 599–604. [Google Scholar] [CrossRef]
  16. Maciak, K.; Dziedzic, A.; Miller, E.; Saluk-Bijak, J. miR-155 as an Important Regulator of Multiple Sclerosis Pathogenesis. A Review. Int. J. Mol. Sci. 2021, 22, 4332. [Google Scholar] [CrossRef]
  17. Piotrzkowska, D.; Miller, E.; Kucharska, E.; Niwald, M.; Majsterek, I. Association of miRNA and mRNA Levels of the Clinical Onset of Multiple Sclerosis Patients. Biology 2021, 10, 554. [Google Scholar] [CrossRef]
  18. Tripathi, A.; Pandit, I.; Perles, A.; Zhou, Y.; Cheng, F.; Dutta, R. Identifying miRNAs in multiple sclerosis gray matter lesions that correlate with atrophy measures. Ann. Clin. Transl. Neurol. 2021, 8, 1279–1291. [Google Scholar] [CrossRef]
  19. Varma-Doyle, A.V.; Lukiw, W.J.; Zhao, Y.; Lovera, J.; Devier, D. A hypothesis-generating scoping review of miRs identified in both multiple sclerosis and dementia, their protein targets, and miR signaling pathways. J. Neurol. Sci. 2020, 420, 117202. [Google Scholar] [CrossRef]
  20. Gao, Y.; Han, D.; Feng, J. MicroRNA in multiple sclerosis. Clin. Chim. Acta 2021, 516, 92–99. [Google Scholar] [CrossRef]
  21. Wang, H. MicroRNAs, Multiple Sclerosis, and Depression. Int. J. Mol. Sci. 2021, 22, 7802. [Google Scholar] [CrossRef] [PubMed]
  22. Yang, Q.; Pan, W.; Qian, L. Identification of the miRNA–mRNA regulatory network in multiple sclerosis. Neurol. Res. 2016, 39, 142–151. [Google Scholar] [CrossRef] [PubMed]
  23. Zingale, V.D.; Gugliandolo, A.; Mazzon, E. MiR-155: An Important Regulator of Neuroinflammation. Int. J. Mol. Sci. 2021, 23, 90. [Google Scholar] [CrossRef] [PubMed]
  24. Baulina, N.; Kiselev, I.; Favorova, O. Imprinted Genes and Multiple Sclerosis: What Do We Know? Int. J. Mol. Sci. 2021, 22, 1346. [Google Scholar] [CrossRef] [PubMed]
  25. Atambayeva, S.; Niyazova, R.; Ivashchenko, A.; Pyrkova, A.; Pinsky, I.; Akimniyazova, A.; Labeit, S. The Binding Sites of miR-619-5p in the mRNAs of Human and Orthologous Genes. BMC Genom. 2017, 18, 428. [Google Scholar] [CrossRef] [Green Version]
  26. Londin, E.; Loher, P.; Telonis, A.G.; Quann, K.; Clark, P.; Jing, Y.; Hatzimichael, E.; Kirino, Y.; Honda, S.; Lally, M.; et al. Analysis of 13 cell types reveals evidence for the expression of numerous novel primate- and tissue-specific microRNAs. Proc. Natl. Acad. Sci. USA 2015, 112, E1106–E1115. [Google Scholar] [CrossRef] [Green Version]
  27. Backes, C.; Meder, B.; Hart, M.; Ludwig, N.; Leidinger, P.; Vogel, B.; Galata, V.; Roth, P.; Menegatti, J.; Grasser, F.A.; et al. Prioritizing and selecting likely novel miRNAs from NGS data. Nucleic Acids Res. 2015, 44, e53. [Google Scholar] [CrossRef] [Green Version]
  28. Wang, J.; Zhang, P.; Lu, Y.; Li, Y.; Zheng, Y.; Kan, Y.; Chen, R.; He, S. piRBase: A comprehensive database of piRNA sequences. Nucleic Acids Res. 2018, 47, D175–D180. [Google Scholar] [CrossRef] [Green Version]
  29. Kim, V.N. Small RNAs just got bigger: Piwi-interacting RNAs (piRNAs) in mammalian testes. Genes Dev. 2006, 20, 1993–1997. [Google Scholar] [CrossRef] [Green Version]
  30. Huang, X.; Wong, G. An old weapon with a new function: PIWI-interacting RNAs in neurodegenerative diseases. Transl. Neurodegener. 2021, 10, 9. [Google Scholar] [CrossRef]
  31. Belkozhayev, A.; Niyazova, R.; Wilson, C.; Jainakbayev, N.; Pyrkova, A.; Ashirbekov, Y.; Akimniyazova, A.; Sharipov, K.; Ivashchenko, A. Bioinformatics Analysis of the Interaction of miRNAs and piRNAs with Human mRNA Genes Having di- and Trinucleotide Repeats. Genes 2022, 13, 800. [Google Scholar] [CrossRef] [PubMed]
  32. Ivashchenko, A.; Pyrkova, A.; Niyazova, R.; Alybayeva, A.; Baskakov, K. Prediction of miRNA binding sites in mRNA. Bioinformation 2016, 12, 237–240. [Google Scholar] [CrossRef]
  33. Friedman, R.A.; Honig, B.A. Free Energy Analysis of Nucleic Acid Base Stacking in Aqueous Solution. Biophys. J. 1995, 69, 1528–1535. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Garg, A.; Heinemann, U. A novel form of RNA double helix based on G·U and C·A+ wobble base pairing. RNA 2018, 24, 209–218. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Kool, E.T. Hydrogen Bonding, Base Stacking, and Steric Effects in DNA Replication. Annu. Rev. Biophys. Biomol. Struct. 2001, 30, 1–22. [Google Scholar] [CrossRef] [Green Version]
  36. Leontis, N.B.; Stombaugh, J.; Westhof, E. The non-Watson-Crick base pairs and their associated isostericity matrices. Nucleic Acids Res. 2002, 30, 3497–3531. [Google Scholar] [CrossRef]
  37. Kondybayeva, A.; Akimniyazova, A.N.; Kamenova, S.U.; Ivashchenko, A. The characteristics of mirna binding sites in mRNA of ZFHX3 gene And its orthologs. Vavilov J. Genet. Breed. 2018, 22, 438–444. [Google Scholar] [CrossRef] [Green Version]
  38. Aisina, D.; Niyazova, R.; Atambayeva, S.; Ivashchenko, A. Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer subtypes. PeerJ 2019, 7, e8049. [Google Scholar] [CrossRef] [Green Version]
  39. Kondybayeva, A.; Akimniyazova, A.; Kamenova, S.; Duchshanova, G.; Aisina, D.; Goncharova, A.; Ivashchenko, A. Prediction of miRNA interaction with mRNA of stroke candidate genes. Neurol. Sci. 2019, 41, 799–808. [Google Scholar] [CrossRef]
  40. Mukushkina, D.; Aisina, D.; Pyrkova, A.; Ryskulova, A.; Labeit, S.; Ivashchenko, A. In silico Prediction of miRNA Interactions With Candidate Atherosclerosis Gene mRNAs. Front. Genet. 2020, 11, 605054. [Google Scholar] [CrossRef]
  41. Kamenova, S.; Aralbayeva, A.; Kondybayeva, A.; Akimniyazova, A.; Pyrkova, A.; Ivashchenko, A. Evolutionary Changes in the Interaction of miRNA With mRNA of Candidate Genes for Parkinson’s Disease. Front. Genet. 2021, 12, 647288. [Google Scholar] [CrossRef] [PubMed]
  42. Akimniyazova, A.; Pyrkova, A.; Uversky, V.; Ivashchenko, A. Predicting Associations of miRNAs and Candidate Gastric Cancer Genes for Nanomedicine. Nanomaterials 2021, 11, 691. [Google Scholar] [CrossRef] [PubMed]
  43. Zhou, L.; Lim, M.Y.T.; Kaur, P.; Saj, A.; Bortolamiol-Becet, D.; Gopal, V.; Tolwinski, N.; Tucker-Kellogg, G.; Okamura, K. Importance of miRNA stability and alternative primary miRNA isoforms in gene regulation during Drosophila development. Elife 2018, 7, e38389. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Schemes of miRNA (from miRBase) interaction with mRNAs of MS candidate genes.
Figure 1. Schemes of miRNA (from miRBase) interaction with mRNAs of MS candidate genes.
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Figure 2. Schemes of interaction between piRNA and 5’UTR, CDS, and 3’UTR mRNA of MS candidate genes.
Figure 2. Schemes of interaction between piRNA and 5’UTR, CDS, and 3’UTR mRNA of MS candidate genes.
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Table
Table 1. Characteristics of miRNA interactions with 5′UTR mRNA of MS candidate genes.
Table 1. Characteristics of miRNA interactions with 5′UTR mRNA of MS candidate genes.
GenemiRNAStart of Site, ntΔG, kJ/molΔG/ΔGm, %Length, nt
EVI5ID00296.3p-miR450−1429125
ID01641.3p-miR450−1328924
ID01702.3p-miR450 ÷ 460 (3)−134 ÷ −13889 ÷ 9224
ID01895.5p-miR453−1328924
ID00756.3p-miR454−1259123
ID02064.5p-miR454, 458−129, −13290, 9123
ID02499.3p-miR462−1219321
ID01595.3p-miR470−1159222
KIF21BID03151.3p-miR195−1179520
ID00296.3p-miR241−1388825
ID01641.3p-miR241−1328924
ID01702.3p-miR198−1348924
ID00061.3p-miR204−1299422
ID01848.5p-miR217−1178923
ID00049.5p-miR227−1348924
b-miR-1045-5p232−1199023
b-miR-1094-5p245−1199022
SOX8ID02761.3p-miR15−1349024
ID00278.3p-miR19−1259123
ID01310.3p-miR19−1219222
b-miR-1771-3p19−1219222
Note. In this and other tables, the number of miRNA BSs repeats is given in parentheses. Groups of miRNAs with BSs in different genes or the same gene are marked with the same color. The ÷ sign denotes the change in values in the “from” to “to” interval.
Table
Table 2. Characteristics of miRNA interactions with CDS mRNA of MS candidate genes.
Table 2. Characteristics of miRNA interactions with CDS mRNA of MS candidate genes.
GenemiRNAStart of Site, ntΔG, kJ/molΔG/ΔGm, %Length, nt
EOMESID01702.3p-miR759 ÷ 774 (3)−134 ÷ −13889 ÷ 9224
ID00061.3p-miR761 ÷ 776 (6)−125 ÷ −12991 ÷ 9422
ID00296.3p-miR767−1408925
ID02294.5p-miR763 ÷ 769 (3)−129 ÷ −13688 ÷ 9324
ID00522.5p-miR764−1279123
ID01041.5p-miR770−1329024
ID00457.3p-miR770−1279422
ID01873.3p-miR770, 773−123, −12594, 9521
ID03151.3p-miR770−1159320
ID01106.5p-miR771−1328924
ID02064.5p-miR772, 778−132, −13691, 9423
ID01879.5p-miR772−1239122
miR-3960772−1159220
ID02429.3p-miR773−1259223
ID03367.5p-miR773−1179320
ID01652.3p-miR774−1258923
ID02538.3p-miR774−1219022
ID02499.3p-miR779−1199221
ID02368.3p-miR782−1279123
TNFRSF1Ab-miR-1752-3p825−1109622
b-miR-1441-3p826−1179522
b-miR-1449-3p826−1069322
b-miR-2164-3p827−1069422
b-miR-1189-3p828−1069422
b-miR-1169-3p828−1109622
miR-1273-3p829−1089322
b-miR-2289-3p830−1109622
Table
Table 3. Characteristics of miRNA interactions with 3′UTR mRNA of MS candidate genes.
Table 3. Characteristics of miRNA interactions with 3′UTR mRNA of MS candidate genes.
GenemiRNAStart of Site, ntΔG, kJ/moleΔG/ΔGm, %Length, nt
ADAM17ID02997.5p-miR5449−1139322
miR-619-5p5466−12110022
b-miR-1367-5p5510−1199722
b-miR-531-5p5511−1049619
b-miR-1641-5p5512−989618
b-miR-2038-5p5512−989618
b-miR-1246-3p5522−1049121
miR-1285-5p5524−1049221
AHI1miR-619-5p4546−1109122
b-miR-1620-3p4561−1109322
miR-44524592−1049123
b-miR-754-5p4621−1159622
miR-50964623−11310021
ID02175.3p-miR4729−1139322
b-miR-1367-3p4729−1158923
b-miR-2038-3p4729−1179223
b-miR-2086-3p4753−1238925
b-miR-1367-5p4766−1109022
b-miR-531-5p4767−1049619
b-miR-1641-5p4768−989618
b-miR-2038-5p4768−989618
CD226b-miR-1752-3p5739−1049121
b-miR-1441-3p5740−1109022
b-miR-1449-3p5740−1139821
b-miR-1189-3p5742−1049220
b-miR-1169-3p5742−1089420
miR-1273g-3p5743−1069121
b-miR-2289-3p5744−1089420
b-miR-2087-3p6952−969419
b-miR-1608-3p6964−1199023
ID02175.3p-miR6966−1139322
b-miR-1367-3p6966−1199223
b-miR-2038-3p6966−1159023
b-miR-2086-3p6990−1309425
ID01836.5p-miR7002−1179323
b-miR-1361-5p7003−1139022
b-miR-1367-5p7003−1109022
b-miR-531-5p7004−1029419
b-miR-1131-5p7005−989419
b-miR-1608-5p7006−1029419
miR-1285-5p7017−1089621
miR-13037027−1069122
miR−1273a8624−1199025
ID01838.5p-miR8625−1108824
miR-1273c8626−1109122
b-miR-1246-5p8631−1089321
b-miR-1035-3p8634−1239424
b-miR-1752-3p8642−1089421
b-miR-1441-3p8643−1219822
b-miR-2164-3p8644−1109820
b-miR-1189-3p8645−1109820
b-miR-1169-3p8645−11510020
miR-1273g-3p8646−1159821
b-miR-2289-3p8647−1109620
b-miR-1449-3p8643−1109621
b-miR-2022-3p8643−1069320
miR-5585-5p8726−1109522
b-miR-2083-3p8870−10610020
ID01334.3p-miR8882−1139022
EVI5miR-1277-5p3352−1009224
miR-1277-5p3394−968824
b-miR-1035-3p3789−1178924
b-miR-1752-3p3797−1069321
b-miR-1441-3p3798−1199722
b-miR-2164-3p3799−1089620
b-miR-1169-3p3800−1069320
b-miR-1189-3p3800−1089620
miR-1273g-3p3801−1109521
b-miR-2289-3p3802−1069320
miR-1273f3834−1009619
b-miR-2164-5p3834−1199224
b-miR-1927-5p3838−1089619
miR-1273e3844−1069122
b-miR-624-3p4024−989620
b-miR-1096-3p3972−1029420
b-miR-2083-3p4026−1029620
b-miR-1096-3p5285−1009220
b-miR-1504-5p5298−1198725
b-miR-609-5p5304−1109121
b-miR-1441-3p5498−1159322
b-miR-2164-3p5499−1089620
b-miR-1169-3p5500−1069320
b-miR-1189-3p5500−1089620
miR-1273g-3p5501−11710021
b-miR-2289-3p5502−1069320
miR-1273f5534−1029819
b-miR-2164-5p5534−1259724
miR-1273d5535−1218925
ID01404.5p-miR5538−1139123
b-miR-1791-5p5555−10210019
b-miR-1927-5p5538−1089619
miR-1273e5544−1109522
b-miR-624-3p5586−939220
b-miR-1096-3p5670−1009220
b-miR-2131-3p5681−989420
ID01836.5p-miR7373−1159223
b-miR-1367-5p7374−1139122
b-miR-1361-5p7374−1159222
b-miR-531-5p7375−1049619
b-miR-1131-5p7376−1029819
b-miR-1641-5p7376−989618
b-miR-2038-5p7376−989618
b-miR-1608-5p7377−1049619
ID02199.5p-miR7388−1139023
IL2RAID01334.5p-miR2066−1139122
miR-619-5p2080−1109122
miR-5585-3p2220−1069122
ID01836.5p-miR2304−1159223
b-miR-1367-5p2305−1179522
b-miR-1361-5p2305−1199522
b-miR-531-5p2306−10810019
b-miR-1131-5p2307−1009619
b-miR-1641-5p2307−10210018
b-miR-2038-5p2307−10210018
b-miR-1608-5p2308−10810019
miR-1285-5p2319−1049221
MGAT5miR-1073029−1109123
ID01261.5p-miR4768−1109320
ID00436.3p-miR4957 ÷ 4983 (14)−104 ÷ −10689 ÷ 9123
ID01030.3p-miR4957 ÷ 4981 (13)−1088923
miR-4664957 ÷ 4983 (14)−106 ÷ −10891 ÷ 9323
Table
Table 4. Characteristics of interaction between piRNA and CDS mRNA of MS genes.
Table 4. Characteristics of interaction between piRNA and CDS mRNA of MS genes.
GenemiRNAStart of Site, ntΔG, kJ/molΔG/ΔGm, %Length, nt
TNFRSF1ApiR-10936874−1498731
piR-10886875−1599430
piR-10885875−1448730
piR-1248874−1449229
piR-10873875−1499130
piR-10935875−1428730
piR-10934875−1408830
piR-14091882−1328928
piR-15670882−1429128
piR-9994900−1448630
piR-9059 900−1599530
piR-9036901−1408729
piR-3586 903−1558832
piR-7244906−1449129
piR-5505937−1579430
piR-2344942−1499230
piR-15406946−1448830
Table
Table 5. Characteristics of interaction between piRNA and 3’UTR mRNA of MS genes.
Table 5. Characteristics of interaction between piRNA and 3’UTR mRNA of MS genes.
GenemiRNAStart of Site, ntΔG, kJ/molΔG/ΔGm, %Length, nt
ADAM17piR-163153501−1498930
piR-52953508−1468531
piR-53013509−1499230
piR-53003509−1449230
piR-53033509−1428930
piR-52943509−1408530
piR-62363509−1469130
piR-76373509−1539530
piR-53583509−1448830
AHI1piR-163154455−1448630
piR-52954461−1428331
piR-53014462−1499230
piR-53004462−1449230
piR-53034462−1428930
piR-52944462−1428630
piR-62364462−1469130
piR-76374462−1469130
piR-53584462−1519230
piR-62444494−1448631
piR-57444494−1448731
piR-71024500−1599331
piR-71054499−1538632
piR-71074499−1518632
piR-17484542−1499330
piR-67464623−1428531
piR-38334645−1408829
piR-136414662−1429328
piR-136344663−1409627
EVI5piR-48153354−1428431
piR-109363378−1448531
piR-108863379−1428430
piR-108853379−1408530
piR-99943404−1539130
piR-90593404−16810030
piR-90363405−1428829
piR-35863407−1639332
piR-72443410−1539629
piR-55053441−1498930
piR-23443446−1519330
piR-12543517−1448332
piR-126233534−1448830
piR-41123543−1409029
piR-123403546−1409028
piR-176173550−1348729
piR-123973572−1429228
piR-123463570−1468531
piR-123993573−1559728
piR-55053577−1428530
piR-154053586−1559430
piR-142393586−1499130
piR-154043587−1499329
piR-146253598−1469130
piR-48154833−1468631
piR-48164833−1428331
piR-12544829−1579032
piR-126234847−1519230
piR-41124857−1409029
piR-123404859−1449328
piR-114214871−1448630
piR-112454872−1429229
piR-123464883−1448431
piR-123994886−1449128
piR-55054890−1408430
piR-154054899−1468830
piR-142394899−1408630
piR-154044900−1408829
piR-12485078−1429129
piR-108735078−1448830
piR-115965079−1428830
piR-109365078−1539031
piR-108865079−1579330
piR-108855079−1428630
piR-108735079−1539430
piR-109355079−1469030
piR-109345079−1449130
piR-109335079−1408830
piR-140915086−1328928
piR-99945104−1539130
piR-90595104−1619630
piR-90365105−1499229
piR-35865107−1689532
piR-72445110−1519529
piR-35875110−1429129
piR-12545215−1579032
piR-126235232−1448830
piR-176175248−1308529
piR-114215252−1408430
piR-123465268−1448431
piR-123995271−1428928
piR-154055284−1519130
piR-142395284−1448830
piR-154045285−1499329
piR-146255296−1499230
piR-94606836−1448731
piR-67466872−1428531
piR-57446872−1408531
piR-71076877−1468332
piR-71026878−1408131
IL12BpiR-163151779−1448630
piR-94601783−1448731
piR-67461818−1468731
piR-152781822−1539131
piR-61051824−1448431
piR-71021824−1408131
piR-85611824−1408132
piR-163152076−1408430
piR-62362084−1448930
piR-57442115−1408531
piR-71072120−1498432
piR-71022121−1428331
piR-148832143−1468631
piR-148842145−1368529
piR-94602213−1499031
piR-131232215−1469130
piR-53632217−1409029
piR-82262233−1428829
piR-57442248−1468831
piR-67462248−1519031
piR-71072253−1468332
piR-152782252−1428531
piR-61052254−1448431
piR-71022254−1468531
piR-85612254−1518832
piR-60922254−1408231
piR-85612255−1498632
piR-2252296−1429128
piR-149742304−1369126
piR-160752310−1389228
MLANApiR-1254976−1518732
piR-4815980−1408231
piR-13770989−1429328
piR-13714998−1449727
piR-12481005−1408929
piR-115961006−1408730
piR-109361005−1518931
piR-108861006−1559130
piR-108851006−1468830
piR-108731006−1519230
piR-109351006−1448830
piR-109341006−1428930
piR-109301006−1428830
piR-156701013−1368628
piR-99941031−1428530
piR-90591031−1579430
piR-35861034−1538732
piR-72441037−1428929
piR-55051068−1579430
piR-23441073−1428830
piR-154061077−1579630
piR-12541142−1558932
piR-126231159−1469030
piR-123401171−1449328
piR-176171175−1328629
piR-114211183−1428530
piR-123461195−1579131
piR-123991198−1499328
piR-154051211−1559430
piR-142391211−1499130
piR-154041212−1499329
piR-146251223−1448930
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MDPI and ACS Style

Kamenova, S.; Sharapkhanova, A.; Akimniyazova, A.; Kuzhybayeva, K.; Kondybayeva, A.; Rakhmetullina, A.; Pyrkova, A.; Ivashchenko, A. piRNA and miRNA Can Suppress the Expression of Multiple Sclerosis Candidate Genes. Nanomaterials 2023, 13, 22. https://doi.org/10.3390/nano13010022

AMA Style

Kamenova S, Sharapkhanova A, Akimniyazova A, Kuzhybayeva K, Kondybayeva A, Rakhmetullina A, Pyrkova A, Ivashchenko A. piRNA and miRNA Can Suppress the Expression of Multiple Sclerosis Candidate Genes. Nanomaterials. 2023; 13(1):22. https://doi.org/10.3390/nano13010022

Chicago/Turabian Style

Kamenova, Saltanat, Aksholpan Sharapkhanova, Aigul Akimniyazova, Karlygash Kuzhybayeva, Aida Kondybayeva, Aizhan Rakhmetullina, Anna Pyrkova, and Anatoliy Ivashchenko. 2023. "piRNA and miRNA Can Suppress the Expression of Multiple Sclerosis Candidate Genes" Nanomaterials 13, no. 1: 22. https://doi.org/10.3390/nano13010022

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