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Nanomaterials logoLink to Nanomaterials
. 2022 Dec 21;13(1):22. doi: 10.3390/nano13010022

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

Saltanat Kamenova 1, Aksholpan Sharapkhanova 2, Aigul Akimniyazova 1, Karlygash Kuzhybayeva 1, Aida Kondybayeva 1, Aizhan Rakhmetullina 3,4, Anna Pyrkova 1,5, Anatoliy Ivashchenko 5,*
Editors: Bing Yan, Sungha Park
PMCID: PMC9823834  PMID: 36615932

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.

Keywords: multiple sclerosis, genes, miRNA, piRNA, diagnosis

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.

Table 1.

Characteristics of miRNA interactions with 5′UTR mRNA of MS candidate genes.

Gene miRNA Start of Site, nt ΔG, kJ/mol ΔG/ΔGm, % Length, nt
EVI5 ID00296.3p-miR 450 −142 91 25
ID01641.3p-miR 450 −132 89 24
ID01702.3p-miR 450 ÷ 460 (3) −134 ÷ −138 89 ÷ 92 24
ID01895.5p-miR 453 −132 89 24
ID00756.3p-miR 454 −125 91 23
ID02064.5p-miR 454, 458 −129, −132 90, 91 23
ID02499.3p-miR 462 −121 93 21
ID01595.3p-miR 470 −115 92 22
KIF21B ID03151.3p-miR 195 −117 95 20
ID00296.3p-miR 241 −138 88 25
ID01641.3p-miR 241 −132 89 24
ID01702.3p-miR 198 −134 89 24
ID00061.3p-miR 204 −129 94 22
ID01848.5p-miR 217 −117 89 23
ID00049.5p-miR 227 −134 89 24
b-miR-1045-5p 232 −119 90 23
b-miR-1094-5p 245 −119 90 22
SOX8 ID02761.3p-miR 15 −134 90 24
ID00278.3p-miR 19 −125 91 23
ID01310.3p-miR 19 −121 92 22
b-miR-1771-3p 19 −121 92 22

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.

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.

Table 2.

Characteristics of miRNA interactions with CDS mRNA of MS candidate genes.

Gene miRNA Start of Site, nt ΔG, kJ/mol ΔG/ΔGm, % Length, nt
EOMES ID01702.3p-miR 759 ÷ 774 (3) −134 ÷ −138 89 ÷ 92 24
ID00061.3p-miR 761 ÷ 776 (6) −125 ÷ −129 91 ÷ 94 22
ID00296.3p-miR 767 −140 89 25
ID02294.5p-miR 763 ÷ 769 (3) −129 ÷ −136 88 ÷ 93 24
ID00522.5p-miR 764 −127 91 23
ID01041.5p-miR 770 −132 90 24
ID00457.3p-miR 770 −127 94 22
ID01873.3p-miR 770, 773 −123, −125 94, 95 21
ID03151.3p-miR 770 −115 93 20
ID01106.5p-miR 771 −132 89 24
ID02064.5p-miR 772, 778 −132, −136 91, 94 23
ID01879.5p-miR 772 −123 91 22
miR-3960 772 −115 92 20
ID02429.3p-miR 773 −125 92 23
ID03367.5p-miR 773 −117 93 20
ID01652.3p-miR 774 −125 89 23
ID02538.3p-miR 774 −121 90 22
ID02499.3p-miR 779 −119 92 21
ID02368.3p-miR 782 −127 91 23
TNFRSF1A b-miR-1752-3p 825 −110 96 22
b-miR-1441-3p 826 −117 95 22
b-miR-1449-3p 826 −106 93 22
b-miR-2164-3p 827 −106 94 22
b-miR-1189-3p 828 −106 94 22
b-miR-1169-3p 828 −110 96 22
miR-1273-3p 829 −108 93 22
b-miR-2289-3p 830 −110 96 22

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.

Table 3.

Characteristics of miRNA interactions with 3′UTR mRNA of MS candidate genes.

Gene miRNA Start of Site, nt ΔG, kJ/mole ΔG/ΔGm, % Length, nt
ADAM17 ID02997.5p-miR 5449 −113 93 22
miR-619-5p 5466 −121 100 22
b-miR-1367-5p 5510 −119 97 22
b-miR-531-5p 5511 −104 96 19
b-miR-1641-5p 5512 −98 96 18
b-miR-2038-5p 5512 −98 96 18
b-miR-1246-3p 5522 −104 91 21
miR-1285-5p 5524 −104 92 21
AHI1 miR-619-5p 4546 −110 91 22
b-miR-1620-3p 4561 −110 93 22
miR-4452 4592 −104 91 23
b-miR-754-5p 4621 −115 96 22
miR-5096 4623 −113 100 21
ID02175.3p-miR 4729 −113 93 22
b-miR-1367-3p 4729 −115 89 23
b-miR-2038-3p 4729 −117 92 23
b-miR-2086-3p 4753 −123 89 25
b-miR-1367-5p 4766 −110 90 22
b-miR-531-5p 4767 −104 96 19
b-miR-1641-5p 4768 −98 96 18
b-miR-2038-5p 4768 −98 96 18
CD226 b-miR-1752-3p 5739 −104 91 21
b-miR-1441-3p 5740 −110 90 22
b-miR-1449-3p 5740 −113 98 21
b-miR-1189-3p 5742 −104 92 20
b-miR-1169-3p 5742 −108 94 20
miR-1273g-3p 5743 −106 91 21
b-miR-2289-3p 5744 −108 94 20
b-miR-2087-3p 6952 −96 94 19
b-miR-1608-3p 6964 −119 90 23
ID02175.3p-miR 6966 −113 93 22
b-miR-1367-3p 6966 −119 92 23
b-miR-2038-3p 6966 −115 90 23
b-miR-2086-3p 6990 −130 94 25
ID01836.5p-miR 7002 −117 93 23
b-miR-1361-5p 7003 −113 90 22
b-miR-1367-5p 7003 −110 90 22
b-miR-531-5p 7004 −102 94 19
b-miR-1131-5p 7005 −98 94 19
b-miR-1608-5p 7006 −102 94 19
miR-1285-5p 7017 −108 96 21
miR-1303 7027 −106 91 22
miR−1273a 8624 −119 90 25
ID01838.5p-miR 8625 −110 88 24
miR-1273c 8626 −110 91 22
b-miR-1246-5p 8631 −108 93 21
b-miR-1035-3p 8634 −123 94 24
b-miR-1752-3p 8642 −108 94 21
b-miR-1441-3p 8643 −121 98 22
b-miR-2164-3p 8644 −110 98 20
b-miR-1189-3p 8645 −110 98 20
b-miR-1169-3p 8645 −115 100 20
miR-1273g-3p 8646 −115 98 21
b-miR-2289-3p 8647 −110 96 20
b-miR-1449-3p 8643 −110 96 21
b-miR-2022-3p 8643 −106 93 20
miR-5585-5p 8726 −110 95 22
b-miR-2083-3p 8870 −106 100 20
ID01334.3p-miR 8882 −113 90 22
EVI5 miR-1277-5p 3352 −100 92 24
miR-1277-5p 3394 −96 88 24
b-miR-1035-3p 3789 −117 89 24
b-miR-1752-3p 3797 −106 93 21
b-miR-1441-3p 3798 −119 97 22
b-miR-2164-3p 3799 −108 96 20
b-miR-1169-3p 3800 −106 93 20
b-miR-1189-3p 3800 −108 96 20
miR-1273g-3p 3801 −110 95 21
b-miR-2289-3p 3802 −106 93 20
miR-1273f 3834 −100 96 19
b-miR-2164-5p 3834 −119 92 24
b-miR-1927-5p 3838 −108 96 19
miR-1273e 3844 −106 91 22
b-miR-624-3p 4024 −98 96 20
b-miR-1096-3p 3972 −102 94 20
b-miR-2083-3p 4026 −102 96 20
b-miR-1096-3p 5285 −100 92 20
b-miR-1504-5p 5298 −119 87 25
b-miR-609-5p 5304 −110 91 21
b-miR-1441-3p 5498 −115 93 22
b-miR-2164-3p 5499 −108 96 20
b-miR-1169-3p 5500 −106 93 20
b-miR-1189-3p 5500 −108 96 20
miR-1273g-3p 5501 −117 100 21
b-miR-2289-3p 5502 −106 93 20
miR-1273f 5534 −102 98 19
b-miR-2164-5p 5534 −125 97 24
miR-1273d 5535 −121 89 25
ID01404.5p-miR 5538 −113 91 23
b-miR-1791-5p 5555 −102 100 19
b-miR-1927-5p 5538 −108 96 19
miR-1273e 5544 −110 95 22
b-miR-624-3p 5586 −93 92 20
b-miR-1096-3p 5670 −100 92 20
b-miR-2131-3p 5681 −98 94 20
ID01836.5p-miR 7373 −115 92 23
b-miR-1367-5p 7374 −113 91 22
b-miR-1361-5p 7374 −115 92 22
b-miR-531-5p 7375 −104 96 19
b-miR-1131-5p 7376 −102 98 19
b-miR-1641-5p 7376 −98 96 18
b-miR-2038-5p 7376 −98 96 18
b-miR-1608-5p 7377 −104 96 19
ID02199.5p-miR 7388 −113 90 23
IL2RA ID01334.5p-miR 2066 −113 91 22
miR-619-5p 2080 −110 91 22
miR-5585-3p 2220 −106 91 22
ID01836.5p-miR 2304 −115 92 23
b-miR-1367-5p 2305 −117 95 22
b-miR-1361-5p 2305 −119 95 22
b-miR-531-5p 2306 −108 100 19
b-miR-1131-5p 2307 −100 96 19
b-miR-1641-5p 2307 −102 100 18
b-miR-2038-5p 2307 −102 100 18
b-miR-1608-5p 2308 −108 100 19
miR-1285-5p 2319 −104 92 21
MGAT5 miR-107 3029 −110 91 23
ID01261.5p-miR 4768 −110 93 20
ID00436.3p-miR 4957 ÷ 4983 (14) −104 ÷ −106 89 ÷ 91 23
ID01030.3p-miR 4957 ÷ 4981 (13) −108 89 23
miR-466 4957 ÷ 4983 (14) −106 ÷ −108 91 ÷ 93 23

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).

Figure 1.

Figure 1

Schemes of miRNA (from miRBase) interaction with mRNAs of MS candidate genes.

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.

Figure 2.

Figure 2

Schemes of interaction between piRNA and 5’UTR, CDS, and 3’UTR mRNA of MS candidate genes.

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.

Table 4.

Characteristics of interaction between piRNA and CDS mRNA of MS genes.

Gene miRNA Start of Site, nt ΔG, kJ/mol ΔG/ΔGm, % Length, nt
TNFRSF1A piR-10936 874 −149 87 31
piR-10886 875 −159 94 30
piR-10885 875 −144 87 30
piR-1248 874 −144 92 29
piR-10873 875 −149 91 30
piR-10935 875 −142 87 30
piR-10934 875 −140 88 30
piR-14091 882 −132 89 28
piR-15670 882 −142 91 28
piR-9994 900 −144 86 30
piR-9059 900 −159 95 30
piR-9036 901 −140 87 29
piR-3586 903 −155 88 32
piR-7244 906 −144 91 29
piR-5505 937 −157 94 30
piR-2344 942 −149 92 30
piR-15406 946 −144 88 30

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).

Table 5.

Characteristics of interaction between piRNA and 3’UTR mRNA of MS genes.

Gene miRNA Start of Site, nt ΔG, kJ/mol ΔG/ΔGm, % Length, nt
ADAM17 piR-16315 3501 −149 89 30
piR-5295 3508 −146 85 31
piR-5301 3509 −149 92 30
piR-5300 3509 −144 92 30
piR-5303 3509 −142 89 30
piR-5294 3509 −140 85 30
piR-6236 3509 −146 91 30
piR-7637 3509 −153 95 30
piR-5358 3509 −144 88 30
AHI1 piR-16315 4455 −144 86 30
piR-5295 4461 −142 83 31
piR-5301 4462 −149 92 30
piR-5300 4462 −144 92 30
piR-5303 4462 −142 89 30
piR-5294 4462 −142 86 30
piR-6236 4462 −146 91 30
piR-7637 4462 −146 91 30
piR-5358 4462 −151 92 30
piR-6244 4494 −144 86 31
piR-5744 4494 −144 87 31
piR-7102 4500 −159 93 31
piR-7105 4499 −153 86 32
piR-7107 4499 −151 86 32
piR-1748 4542 −149 93 30
piR-6746 4623 −142 85 31
piR-3833 4645 −140 88 29
piR-13641 4662 −142 93 28
piR-13634 4663 −140 96 27
EVI5 piR-4815 3354 −142 84 31
piR-10936 3378 −144 85 31
piR-10886 3379 −142 84 30
piR-10885 3379 −140 85 30
piR-9994 3404 −153 91 30
piR-9059 3404 −168 100 30
piR-9036 3405 −142 88 29
piR-3586 3407 −163 93 32
piR-7244 3410 −153 96 29
piR-5505 3441 −149 89 30
piR-2344 3446 −151 93 30
piR-1254 3517 −144 83 32
piR-12623 3534 −144 88 30
piR-4112 3543 −140 90 29
piR-12340 3546 −140 90 28
piR-17617 3550 −134 87 29
piR-12397 3572 −142 92 28
piR-12346 3570 −146 85 31
piR-12399 3573 −155 97 28
piR-5505 3577 −142 85 30
piR-15405 3586 −155 94 30
piR-14239 3586 −149 91 30
piR-15404 3587 −149 93 29
piR-14625 3598 −146 91 30
piR-4815 4833 −146 86 31
piR-4816 4833 −142 83 31
piR-1254 4829 −157 90 32
piR-12623 4847 −151 92 30
piR-4112 4857 −140 90 29
piR-12340 4859 −144 93 28
piR-11421 4871 −144 86 30
piR-11245 4872 −142 92 29
piR-12346 4883 −144 84 31
piR-12399 4886 −144 91 28
piR-5505 4890 −140 84 30
piR-15405 4899 −146 88 30
piR-14239 4899 −140 86 30
piR-15404 4900 −140 88 29
piR-1248 5078 −142 91 29
piR-10873 5078 −144 88 30
piR-11596 5079 −142 88 30
piR-10936 5078 −153 90 31
piR-10886 5079 −157 93 30
piR-10885 5079 −142 86 30
piR-10873 5079 −153 94 30
piR-10935 5079 −146 90 30
piR-10934 5079 −144 91 30
piR-10933 5079 −140 88 30
piR-14091 5086 −132 89 28
piR-9994 5104 −153 91 30
piR-9059 5104 −161 96 30
piR-9036 5105 −149 92 29
piR-3586 5107 −168 95 32
piR-7244 5110 −151 95 29
piR-3587 5110 −142 91 29
piR-1254 5215 −157 90 32
piR-12623 5232 −144 88 30
piR-17617 5248 −130 85 29
piR-11421 5252 −140 84 30
piR-12346 5268 −144 84 31
piR-12399 5271 −142 89 28
piR-15405 5284 −151 91 30
piR-14239 5284 −144 88 30
piR-15404 5285 −149 93 29
piR-14625 5296 −149 92 30
piR-9460 6836 −144 87 31
piR-6746 6872 −142 85 31
piR-5744 6872 −140 85 31
piR-7107 6877 −146 83 32
piR-7102 6878 −140 81 31
IL12B piR-16315 1779 −144 86 30
piR-9460 1783 −144 87 31
piR-6746 1818 −146 87 31
piR-15278 1822 −153 91 31
piR-6105 1824 −144 84 31
piR-7102 1824 −140 81 31
piR-8561 1824 −140 81 32
piR-16315 2076 −140 84 30
piR-6236 2084 −144 89 30
piR-5744 2115 −140 85 31
piR-7107 2120 −149 84 32
piR-7102 2121 −142 83 31
piR-14883 2143 −146 86 31
piR-14884 2145 −136 85 29
piR-9460 2213 −149 90 31
piR-13123 2215 −146 91 30
piR-5363 2217 −140 90 29
piR-8226 2233 −142 88 29
piR-5744 2248 −146 88 31
piR-6746 2248 −151 90 31
piR-7107 2253 −146 83 32
piR-15278 2252 −142 85 31
piR-6105 2254 −144 84 31
piR-7102 2254 −146 85 31
piR-8561 2254 −151 88 32
piR-6092 2254 −140 82 31
piR-8561 2255 −149 86 32
piR-225 2296 −142 91 28
piR-14974 2304 −136 91 26
piR-16075 2310 −138 92 28
MLANA piR-1254 976 −151 87 32
piR-4815 980 −140 82 31
piR-13770 989 −142 93 28
piR-13714 998 −144 97 27
piR-1248 1005 −140 89 29
piR-11596 1006 −140 87 30
piR-10936 1005 −151 89 31
piR-10886 1006 −155 91 30
piR-10885 1006 −146 88 30
piR-10873 1006 −151 92 30
piR-10935 1006 −144 88 30
piR-10934 1006 −142 89 30
piR-10930 1006 −142 88 30
piR-15670 1013 −136 86 28
piR-9994 1031 −142 85 30
piR-9059 1031 −157 94 30
piR-3586 1034 −153 87 32
piR-7244 1037 −142 89 29
piR-5505 1068 −157 94 30
piR-2344 1073 −142 88 30
piR-15406 1077 −157 96 30
piR-1254 1142 −155 89 32
piR-12623 1159 −146 90 30
piR-12340 1171 −144 93 28
piR-17617 1175 −132 86 29
piR-11421 1183 −142 85 30
piR-12346 1195 −157 91 31
piR-12399 1198 −149 93 28
piR-15405 1211 −155 94 30
piR-14239 1211 −149 91 30
piR-15404 1212 −149 93 29
piR-14625 1223 −144 89 30

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.

Data Availability Statement

Data are contained within the present article.

Conflicts of Interest

The authors declare no conflict of interest.

Funding Statement

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

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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