Genetic Alteration of MicroRNA Affecting Cancer Pathways

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GENETIC ALTERATION OF MICRORNA AFFECTING CANCER PATHWAYS

15

Flavia Scoyni1, Vincenzo Bonnici2, Alfredo Pulvirenti3, Rosalba Giugno2,* 1University

of Eastern Finland, Kuopio, Finland; 2University of Verona, Verona, Italy; 3University of Catania, Catania, Italy

CHAPTER OUTLINE Introduction������������������������������������������������������������������������������������������������������������������������������������������ 270 Biological Evidence of MicroRNA Mutations and Diseases Associations��������������������������������������������������� 271 Single-Nucleotide Polymorphism Affecting MicroRNA Transcription��������������������������������������������������������� 273 Single-Nucleotide Polymorphism Affecting MicroRNA Maturation������������������������������������������������������������ 273 Single Nucleotide Polymorphism Affecting MicroRNA Targeting��������������������������������������������������������������� 275 Computational Resources to Study Phenotypes at System Level��������������������������������������������������������������� 276 Computational Resources to Study the Single-Nucleotide Polymorphisms Effects on Cancer��������������������� 277 Conclusion������������������������������������������������������������������������������������������������������������������������������������������� 282 List of Acronyms and Abbreviations�������������������������������������������������������������������������������������������������������� 282 Glossary����������������������������������������������������������������������������������������������������������������������������������������������� 282 References������������������������������������������������������������������������������������������������������������������������������������������� 283

CHAPTER OUTLINE This chapter starts with a description of microRNA (miRNA or miR) biogenesis and physiological role of miRNAs on diseases. Then, it presents the in vivo heterogeneity of miRNAs and the biological evidence of miRNA mutations and cancer associations reported in literature accurately classified according to the nature of mutations such as single-nucleotide polymorphism (SNP) affecting miRNA Transcription, SNP affecting miRNA Maturation, and SNP affecting miRNA Targeting. The chapter continues with a review of knowledge base-driven pathway analysis to study phenotypes at system level. Such methods could make use of the computational tools and online repositories to collect and study the SNPs effects on diseases such as cancer. Finally, a summary concludes the chapter.

*Senior

author.

Cancer and Noncoding RNAs. http://dx.doi.org/10.1016/B978-0-12-811022-5.00015-2 Copyright © 2018 Elsevier Inc. All rights reserved.

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INTRODUCTION MicroRNAs (miRNAs) are endogenous noncoding and small RNA molecules 21–25 nucleotide in length [1]. The physiological role of these RNA molecules consists in regulating gene expression at posttranscriptional level through sequence-specific pairing. Silencing protein complexes are recruited by miRNAs sequence-specific interaction promoting the destabilization of the messenger RNAs (mRNAs) or interfering with translation of the target transcript [2]. In that way, miRNAs can accomplish either silencing or fine-tune regulation of the translation of specific mRNAs. miRNAs control such a great variety of biological processes, including cell proliferation, development, differentiation, and apoptosis [3]. Moreover, dysregulations in miRNAs interactions are associated with the development and progression of complex diseases such as cancer and cancer-related processes [4]. For this reason, miRNAs are identified as a functional class of tumor suppressors or oncogenes. In 2008 Morin et al. described isomiRs as the presence in vivo of different miRNAs originated by the same miRNA transcript [6]. These changes result in a final product different in length and sequence respect to the canonical miRNA. It has been observed a wide range of modification in miRNA precursor molecule (pre-miR) that leads to isomiRs generation; 5′ or 3′ gain or deletion and substitution in the sequence are the most common modification occurring in the pre-miR [7]. Nevertheless, miRNA biogenesis machinery cleavage or exonucleases degradation could produce different miRNA isoform as well [6,8]. Finally, posttranscriptional modification and RNA editing complexes as well as SNP presence in the genomic sequence of the primary miRNA are responsible of variability. When the modification affects the 5′ of the miRNA sequence, it is possible to obtain isomiR with differences in the seed region with respect to the common miRNA sequence. Changes in the seed region are involved in gain of new miRNA functions that could generate a new pool of targets, or a loss of regulation of the natural target [9]. Despite the abundance of isomiR in RNA-seq dataset, their role in cell physiology remains unknown and the biological effect is considered to be strictly related to the difference in the expression within the isomiR population. Since alteration in the 3′ of the miRNA sequence could lead to the production of an isomiR with different out-of-seed complementarity of the target, isomiRs may act in a redundant manner affecting the same pathway of the most common miRNA isoform. In 2011 Cloonan et al. hypothesized that the heterogeneity in the miRNA population could act protecting the miRNA functionality from nonspecific targeting of the pathways [8]. Nevertheless, the variability in the isomiR population represents the attempt of fine-tune regulation in different cell type or cell stages in which a particular isomiRs pattern expression acts as the best suitable one to a specific cell response. Nowadays, computational biologists rely on advanced tools to address difficulties associated with miRNAs’ discovery or target sites’ identification. The prediction of phenotypes, such as diseases, or of responses to therapies widely uses the large amount of genotypic high-dimensional data obtained through next-generation sequencing techniques. High-throughput sequencing and gene profiling techniques are radically transforming medical research, allowing the full monitoring of a biological system. The aim is to analyze the effects of mRNA target SNP on miRNAs’ binding and more generally to investigate the result of mutations in miRNA genes. The recognition operated by the miRNAs is mainly encoded in the seed region (nucleotides 2–7 of the mature miRNAs) that mediates the target identification by sequence complementarity [5]. Therefore, SNPs located in miRNA genes may

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theoretically affect miRNA biogenesis and impact target association. Nevertheless, SNPs and mutations in miRNAs’ target sites may impair the maturation process or the target selection. Furthermore, alterations of target recognition due to SNPs or mutations in the seed regions create new off-target interactions. Consequently, researchers over the years are studying and discovering the associations of genetic mutations affecting miRNA-target interactions and functional pathways leading to several diseases such as chronic lymphocytic leukemia [10], papillary thyroid carcinoma [11], colorectal cancer [12], breast cancer [13], Parkinson disease [14], rheumatoid arthritis [15], systemic lupus erythematosus [16], Crohn disease [17], and psoriasis [18], among the others.

BIOLOGICAL EVIDENCE OF MICRORNA MUTATIONS AND DISEASES ASSOCIATIONS Excluding miRNA processing complexes mutation, SNPs in different miRNAs’ sites could affect the miRNA expression at different level or leading to an ineffective or unspecific target binding (Fig. 15.1). Analyzing genome-wide distribution of miRNA polymorphism in miRNA loci and their targets, Saunders et al. found that polymorphisms are present mostly in the flanking sequences with respect to occurring within the pre-miR sequence [5]. Nevertheless, evaluating the incidence of SNPs inside the pre-miR sequences, they showed a drop in genetic alteration frequency in the mature miRNA seed region. These data demonstrate that miRNAs’ loci are under selective pressure and that SNPs occurring in the seed region are negatively selected with an occurrence of ≈1.3 SNPs per kilobase (Fig. 15.2A). Since the seed region is functionally active in the specificity and efficiency of miR-mediated posttranscriptional regulation, it is not surprising that the occurrence in mutation is decreased with respect to pre-miR flanking region in which the occurrence is ≈3 SNPs per kilobase. Lu et al. confirmed the

FIGURE 15.1 Selective pressure acting in single nucleotide polymorphism (SNP) conservation. (A) Conservation of SNP in microRNA (miRNA) seed and flanking sites. (B) Conservation of SNP in miRNA-target seed and flanking sites.

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FIGURE 15.2 How single-nucleotide polymorphism (SNP) may affect microRNAs (miRNAs) biogenesis and function. (A) SNP in the genomic DNA hosting the miRNAs gene. (B) Affection of the production of pre-miRNA molecule due to SNP presence. (C) Affection of the production of pri-miRNA molecule due to SNP presence. (D) SNP carrying miRNA is loaded in DICER complex impair guide strand selection or target recognition. (E–G) Target RNA SNP affects the regulation mediated by miRNAs.

presence of selective constraints in miRNA loci, associated to their functional role, showing that SNPs occurrence is reduced in disease-related miRNAs respect of other miRNAs [19]. Recently, new studies analyzed the distribution of SNPs within miRNA target sequences. Although experimental data about targets of a specific miRNA are not always available, bioinformatics tools predict miRNA targets. The confidence of bioinformatics tools for miRNA target prediction consists informally in obtaining the same results with different procedures. The occurrence of SNPs in miRNA targets are predicted by several studies by investigating the negative selective pressure in occurrence of SNPs in seed binding region of miRNA targets [5,20]. Polymorphisms in the miRNA target sites seem to be mostly deleterious and so negatively selected (Fig. 15.3). Besides these evidences, Chen et al. and Saunders et al. showed that it is rarely present, as well a recent positive selection pressure on advantageous SNPs in miRNA target binding sites (Fig. 15.2B) [5,20]. Moreover, SNPs occurring in miRNA binding sites flanking regions are present homogenously inside the population respect of SNPs occurring in the miRNA binding sites that show a high level of differentiation [21]. The presence of SNPs in the target binding regions could possibly lead to a match to the seed of new miRNAs (gain of regulation) and not only just be involved in mismatch with the physiological miRNAs (loss of regulation). However, to be biologically relevant, the mutation should be correlated to the expression of the appropriate miRNAs both in space and in time.

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FIGURE 15.3 Percentage of single-nucleotide polymorphism (SNP) affecting different microRNA (miRNA) region. Data collected from miRNASNP v.2.0.

SINGLE-NUCLEOTIDE POLYMORPHISM AFFECTING MICRORNA TRANSCRIPTION Due to the difficulty to identify and validate miRNA unique promoter, few examples of experimentally validated SNPs affecting miRNAs promoter are available. Luo et al., in 2011 were able to associate the presence of SNPs in the promoter region of miR-146a with systemic lupus erythematosus (SLE) occurrence [22]. SNP rs57095329 was able to affect the binding of Ets-1 transcription factor leading to a decrease in transcription of miR-146a. Since they identified miR-146a as a negative regulator of the interferon pathway, they linked the abnormal activation of this pathway to the downregulation of the expression of miR-146a due to the presence of SNP rs57095329 in SLE patients. However, miRNA coding genes affect also at epigenetic level. In this case, the presence of SNP is involved in changing of epigenetic regulation of the transcription of the miRNA and so in the cancer development. For example, in the case of breast cancer the silencing of particular miRNAs is a leading event of cancer insurgence [23].

SINGLE-NUCLEOTIDE POLYMORPHISM AFFECTING MICRORNA MATURATION miRNAs’ DNA regions are transcribed in the nucleus as primary transcripts (pri-miRNAs) that is capped, spliced, and polyadenylated [1]. Then the RNase III type DROSHA additionally processes primiRNA in a hairpin-shaped precursor (pre-miRNA). In 2008, Jazdzewski et al. showed that a common G to C polymorphism in the pre-miR-146a sequence leads to a reduction of the amount of the mature

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and the pre-miR generated from the C allele 1.9- and 1.8-fold, respectively, compared with the G allele [24]. The in vitro processing of the C carrying SNP pri-miR146a decreases, displaying an impairment in DROSHA processing of the molecule. Nevertheless, the C allele presence inhibits the interaction of the pre-miR146a with a nuclear factor generating a deficiency of mature miR146a. Clinically, the less efficient inhibition of target genes of miR146a involved in the Toll-like receptor and cytokine signaling pathway (TRAF6, IRAK1), and PTC1 (also known as CCDC6 or H4) leads to a predisposition of the insurgence of papillary thyroid carcinoma (Fig. 15.4). Exportin-5 is responsible to the export of the precursor in the cytoplasm where RNase III type DICER produces a 22-nucleotide-long miRNA duplex. A single strand of the processed duplex (guide strand) is chosen and loaded in the RNA-induced silencing complex (RISC) to obtain a functional miRNA. Interestingly, in 2009 an American group examined SNP occurrence in X-linked miRNAs [25]. They found that an SNP could lead to a production of a completely different miRNAs respect the wild-type. MiR-934-5p T to G variant occurs at the DROSHA processing site; nevertheless, this particular site is located at the 5′-end region that is essential in strand selection into miRISC complex. Northern blot analysis confirmed an impaired length of the mature miRNA, consistent with the impairment in the Drosha maturation step. At the same time, they detected an increased presence of the antisense of the guide strand in the mature miRNA. Usually, the wild-type guide miRNA strand is most likely to be selected and load into RISC complex due to the lower thermodynamic stability of the 5′-end. The U to G transvection produces an even less stable 5′-end and leads to an altered guide strand selection. SNPs could occur at different level in miRNAs transcription and regulation, but it has also demonstrated that miRNAs’ modification at RNA-editing level leads to impairment in molecule functionality. In 2007, Kawahara et al. demonstrated that ADAR protein is able to modify pri-miR-151 editing A to I affecting DICER-mediated miRNA processing [26]. This abnormal editing leads to a complete

FIGURE 15.4 Correlation of miRNA SNP and cancer development.

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blockage of miR-151 cleavage by Dicer and accumulation of edited pre-miR-151 RNAs. Although the existence of miRNA editing [27–29] is an evolutionally conserved mechanism to allow an assortment in miRNAs and their targets, and it was demonstrated to occur frequently in the cells [30]; the fate of edited pri-miRNAs is mostly unknown.

SINGLE NUCLEOTIDE POLYMORPHISM AFFECTING MICRORNA TARGETING In 2005, Abelson et al. studying the role of the gene SLITRK1 in Tourette syndrome, provided the first evidence that a mutation that impair miRNA-target interaction produced a relevant phenotype [31]. A mutation screening in patients affected by the disease revealed a G to A transition in 3′-UTR region of SLITRK1 gene. The mutation resulted in a durable interaction of the mRNAs with miR-189 and promoted a downregulation of the gene target tougher than physiologically, leading to a loss of function phenotype. SNP rs61764370 is the most studied SNP affecting miRNA binding efficiency and is located in the 3′-UTR region of KRAS gene. G to T transversion in the KRAS 3′-UTR region influences the binding with let-7 miRNA. Since both KRAS and let-7 correlate in cancer development, as oncogene and tumor suppressor miRNAs, SNP rs61764370 may be relevant in cancer. In fact, Chin et al. defined a biological role of this SNP in non–small lung cancer patients [32]. They demonstrate the effectiveness of the SNP in disrupting the miRNA binding site in KRAS 3′-UTR and the consequent overexpression of KRAS oncogene. After this pioneer study, SNP rs61764370 has been associated to the insurgence of many other types of cancer [33] (Fig. 15.5).

FIGURE 15.5 Correlation of microRNA-target SNP and cancer development.

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FIGURE 15.6 Percentage of single-nucleotide polymorphism affecting microRNA target sites in different RNA target.

Recently, it has been demonstrated that miRNA molecules are able to interact with two additional classes of noncoding RNAs: circular RNAs (circRNAs) and long noncoding RNAs (lncRNAs) [34– 37]. mRNAs, lncRNAs, and circRNAs compete for the binding sites of miRNAs and create a complex network of interaction and regulation (Fig. 15.6). For these reason, noncoding RNAs are usually depleted of polymorphisms at miRNAs’ binding sites [24,38–40].

COMPUTATIONAL RESOURCES TO STUDY PHENOTYPES AT SYSTEM LEVEL Great research interest has focused on a class of methods called Knowledge base-driven pathway analysis [41]. Such methods leverage on existing databases, such as the Kyoto Encyclopedia of Gene and Genomes (KEGG) [42] or Pathway Commons [43], to identify those pathways that may be affected by the expression changes in the observed phenotype. Knowledge base-driven pathway analysis techniques can be grouped into three generations of approaches [44]: (1) Over-Representation Analysis (ORA); (2) Functional Class Scoring (FCS); (3) Pathway Topology-based (PT). Within the last class, we can identify two subclasses: the first one which allows to study the effect of a phenotype at the global level; the second, and more recent, which allows to identify subpathways that are specifically related to the phenotype under study. First-generation methods statistically evaluate the number of altered genes in a pathway with respect to the set of all analyzed genes. After filtering the resulting gene set of an expression assessment experiment, ORA strategies typically divide the list of genes according to the pathway each gene belongs to [45–50]. DIANA-miRPath [51], assesses the impact of miRNAs in biological processes by identifying the pathways in which they are significantly involved. The software package performs the functional annotation of one or more miRNAs and allows the identification of subsets of miRNAs, which significantly regulate a collection of pathways. FCS methods compute a gene-level statistic

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from their expression levels, by means of a statistical approach considering all genes in a pathway [52–54]. FCS methods avoid some of the limitations of the ORA approaches by ranking all genes through their expression level and by considering the dependencies within a pathway. The third class of techniques models a pathway as a graph, considering its topology when computing scores [55]. In Draghici et al. [56], an analytical technique called impact factor (IF) was introduced. The IF is a pathway-level score that takes into account biological factors such as the magnitude of change in genes expression, the type of interactions between genes, and their location in the pathway. The method presented in [56] has been further improved by the SPIA algorithm [57]. More recently, Vaske et al. [58] presented the PARADIGM algorithm, which has been further improved by [59]. PARADIGM is a method to infer patient-specific genetic activity by incorporating information regarding interactions between genes provided in a pathway. In [60], authors developed a new approach, Micrographite, which can integrate pathways with predicted and validated miRNA-target interactions. The method, by performing a topological analysis based on expression profiles, can identify significant gene circuits specific to a phenotype. In [61], authors presented MITHrIL (miRNA enriched pathway impact analysis). The strength of MITHrIL lies in the enrichment of pathways with information regarding miRNAs, posttranscriptional regulatory elements whose consideration is clearly essential to the greater reliability of the results. The method, starting from expression values of genes and/or miRNAs, returns a list of pathways sorted according to the degree of their deregulation, together with the corresponding statistical significance (P-values). To represent the underlying biological phenomena more accurately and to identify putative contextspecific communities related to the pathology under study, the last generation of pathway analysis tools shifted the focus toward subpathways (local areas of the entire biological pathway). In [62], authors introduced Subpathway-GM, which uses a structural node similarity within pathways to identify metabolic subpathways based on information from genes and metabolites. In [63], authors introduced TEAK, a topology enrichment analysis tool based on Bayesian Networks, to extract linear and nonlinear subpathways. In [64], authors propose DEsubs to identify differentially expressed subpathways using RNA-seq data. In [65], authors propose CHRONOS which for the first time introduces temporal subpathway searching built on top of mRNA and miRNA expression data. In a near future, the above extensive work will take more and more advances of computational resources that collect and study the SNPs effects in miRNAs.

COMPUTATIONAL RESOURCES TO STUDY THE SINGLE-NUCLEOTIDE POLYMORPHISMS EFFECTS ON CANCER Large-scale in-silico data analysis [5] on the distribution of polymorphisms in the human miRNA genes have shown that miRNA genes have a lower density of SNPs compared to the flanking regions in the genome. The mature miRNA has a lower density of SNPs compared to the precursor, and finally the seed in the mature miRNA has the lowest SNP density. This reflects the functional importance of the binding site. In [66], authors conducted a global analysis of SNPs in miRNA genes by making use of dbSNP (build 137 for human) [67]. Authors identified 1899 SNPs in 961 pre-miRNAs of human genome. Few underlying reasons of the distribution of SNPs in miRNA genes were then conducted. These include (1) the degree of conservation for one miRNA family and the comparison of the SNP density between miRNA groups with different degree of conservation; (2) the average SNP densities

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between clustered and individual miRNAs; (3) the genomic localization of miRNAs in fragile sites, and then investigated the enrichment of the miRNAs with multiple SNPs in fragile sites; the stability of secondary structures of pre-miRNAs with respect to the SNPs frequencies; the relationships between the SNP densities of miRNAs with the number of diseases that they were associated with, and the number of QTLs (Quantitative Trait Loci) they were overlapped with. Such results are coherent with those obtained by Duan et al. [68], which predicted that the highest effects on minimum free energy (MFE) are caused by SNPs within mature sequences, followed by SNPs in the stem regions and loop domains. Gong et al. showed that 44% of the candidate SNPs cause significant changes in the stem-loop structures and are likely to affect production of mature miRNAs [9]. Furthermore, Gong et al. identified 48 SNPs in the seed regions and used miRanda [69] and TargetScan [70] algorithms to predict the changes in miRNA targets. This study demonstrated that SNPs in seed regions lead to creation and disruption of putative binding sites, and the total numbers of putative targets could be drastically altered by these polymorphisms. For example, an SNP (rs5186) located in the binding site of miR-155 can change the expression of a target gene (AGTR1), which is associated with blood pressure. Few computational resources about the polymorphic miRNA regulation have been developed to allow the systematic study of the above effects. These integrate different features of the miRNA-related genetic variants, and facilitate the research in this field. Due to the abundance of candidate SNPs in 3′UTRs, most of the databases included the information about polymorphic target sites, and utilized different prediction methods to assess their effects on miRNA bindings. Table (Table 15.1) reports the data sources and their main features, listed in what follows. In [71], authors presented the PolymiRTS database 3.0 that establishes links between polymorphisms in miRNA target sites and their possible functional impact in biological processes by including gene pathways for human and mouse from the KEGG database. The system allows to highlight the Table 15.1  List of Currently Maintained Resources System

URL

PolymiRTS 3.0

http://compbio.uthsc. edu/miRSNP/ http://bioinfo.life. hust.edu.cn/miRNASNP2/index.php http://bioinfo.bjmu. edu.cn/mirsnp/ search/ http://gyanxet.com/ hno.html http://mirdsnp.ccr. buffalo.edu/ http://compbio.uthsc. edu/SomamiR/ http://driverdb. tms.cmu.edu.tw/ ym500v3/index.php

miRNASNP 2.0

mirSNP

HNOCDB miRdSNP SomamiR 2.0 YM500v3

Genomic Annotations

Functional Annotations

Phenotype Associations

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

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genes with polymorphic miRNA target sites in the context of pathways and to study the SNPs falling in the mRNAs corresponding to the miRNA recognition elements. In [72], authors proposed miRNASNP, which characterizes all known miRNA-related SNPs. Authors also and analyzed the SNPs’ effects on target binding alteration and mature miRNA biogenesis both computationally and experimentally. In particular, the miRNASNP database contains five major modules: SNPs in human pre-miRNAs; SNPs in human pre-miRNA flanking regions; SNPs in premiRNAs of other eight species; targets gain/loss by SNPs in miRNA seeds; and targets gain/loss by SNPs in target 3′-UTRs. The marked SNPs in the pre-miRNA stem-loop are shown into the RNA secondary structure. All information is available through a web application. Recently, in [68], the database has been extended with expression data in different tissues, genome-wide association studies of miRNASNPs and experimentally validate miRNA–mRNA interaction. mirSNP, which has been proposed in [73], collects the genomic variations affecting miRNA target sites and that have been associated with complex diseases by GWAS and expression quantitative trait loci (eQTL). The system combines information from dbSNP [74] 135 and miRBase [75] 18 to extract SNP in predicted miRNA target sites. It provides a web interface for querying the database content that also allows to combine user’s additional GWAS and eQTL data to identify putative miRNA-related SNPs from traits/diseases-associated variants. HNOCDB [76] is a comprehensive database regarding genes and miRNAs relevant to head, neck, and oral cancers. It stores mutations, methylations, and polymorphisms of oncogenes/oncomiRs, as well as expression profiles and chromosomal maps for the three types of cancers that represent the sixth most widespread cancer worldwide (as of 2012) with an average 5-year survival rate of around 50% [77]. The database contains 451 genes and 109 miRNAs supported by evidence compiled from Pubmed records. miRdSNP, proposed in [78], provides a manually curated dataset, obtained from literature, of SNPs functionally related to diseases and falling on the 3′-UTRs of human genes. It is intended as a resource for investigating disease-associated SNPs and their spatial relationship with miRNA target sites. The authors provided dSNP-disease associations on several types of diseases and for each SNP provide its specificity to the pathology. For example, authors identified that Breast cancer has the highest number of dSNPs, followed by type 2 diabetes, schizophrenia, rheumatoid arthritis, obesity, and colorectal cancer. The database also incorporates reference sequence (RefSeq) genes, predicted miRNA target sites, and SNP sequence data into a single consolidated resource. The authors filtered the SNP dataset obtained from UCSC Genome browser by indexing the genomic coordinates located on 3′-UTRs of genes; then authors annotated dSNPs using an in-house developed data pipeline which searches for PubMed articles linked to SNPs. By manually reviewing the literature authors identified 630 dSNPs for 204 human diseases from 754 publications. Then for each dSNP authors captured linkage disequilibrium (LD) information using the data provided by the HapMap project [79]. miRNAs target site were obtained using TargetScan [70] and PicTar [80]. To address the low prediction specificity of the miRNA target prediction algorithms, authors incorporate data from four curation databases (TarBase [81], miRTarBase [82], miRecords [83], and miR2disease [84]). An online interactive visualization tool displays SNPs and miRNA target sites’ densities across human chromosomes. SomamiR 2.0 [85] is a database of somatic mutations altering interactions between miRNAs and competing endogenous RNAs (ceRNA) in cancer [86]. The competing endogenous effect is a posttranscriptional activity in which different RNAs (ceRNAs) compete for shared miRNAs (miRNAs), thus regulating each other. A competing effect occurs when one or more miRNA response elements (MREs),

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targeted by the same pool of miRNAs, lie in an RNA transcript. The cross talk between mRNAs, lncRNA, and circRNA mediated by MREs, regulates the relative concentrations of transcripts within the cell, yielding large-scale regulatory networks. Mutations in miRNAs, especially in their seed regions, and miRNA target sites may alter miRNA-ceRNA interactions and dysregulation in ceRNA networks has shown to play important roles in cancer pathogenesis [87]. The database is divided into six components, four of them relate to mutations in miRNA sequences or miRNA target sites and the other two concern genes and pathways associated with such somatic mutations. The system stores 2423 somatic mutations regarding miRNA sequences, including pre-miRNA and mature miRNA locations, 181 of which are in miRNA seeds. Sequence mutations were collected by integrating whole-exome and whole-genome sequences from several data sources, including TCGA (The Cancer Genome Atlas, https://cancergenome.nih.gov). The system also provides a public instance of mir2GO that can be used to analyze the functional impact of sequence mutations in miRNA seed regions. Somatic mutations in experimentally identified miRNA target sites, retrieved by three newly emerging high-throughput technologies, are split into two sections. Experimental data were obtained from starBase and combined with the COSMIC database mutations. In particular, CLASH (cross-linking, ligation, and sequencing of hybrids) data were scanned to identify 4048 mature transcripts affected by somatic mutations in miRNA target regions. PAR-CLIP (photoactivatable-ribonucleoside-enhanced cross-linking and immunoprecipitation) and HITS-CLIP (high-throughput sequencing of RNA isolated by cross-linking immunoprecipitation) data were integrated to identify affected target sites in mRNAs, lncRNAs, and circRNAs, and grouped into 34 distinct experiments. Application of miRNA target site prediction tools, including TargetScan, PITA, and the six seed matches algorithm, were used to provide 23,460 potentially affected mature ceRNA transcripts. The two additional data sections regard biological pathways in KEGG and a list of genes associated with cancer risk obtained by integrating GWAS and CGAS (candidate gene association studies) from the UCSC Table Browser (https://genome.ucsc.edu/cgi-bin/hgTables), the NHGRI GWAS Catalog (https://www. ebi.ac.uk/gwas/), and the Cancer GAMAdb (http://www.hugenavigator.net/CancerGEMKB/caIntegratorStartPage.do). The two collections regard pathways and genes that are impacted by or contains somatic mutations in miRNA target sites (listed within the above data sections), respectively. YM500 [88] is a database built by analyzing next-generation sequencing data regarding small noncoding RNAs (sncRNA). The system collects from expression data analysis results for sncRNA quantification, arm switching discovery, novel sncRNA prediction, and most important for the purpose of this review, isomiR identification that can be integrated to dbSNP information to distinguish isomiRs from SNPs. MirVar [89] collects manually curated variations from public resources and published literature. By making use of dbSNP authors collected genetic variations in human genome and mapped them to premiRNA loci. Authors also collected manually curated variation in miRNA loci from published literature. Furthermore few miRNAs with posttranscriptional modification such as adenosine to inosine RNA editing [90] were also included. mirVar, is also built on top of Leiden Open (source) Variation Database (LOVD) system. The system provides also a computational pipeline to predict potential effects of variations on miRNA biogenesis and its functions. This pipeline makes use of PHDcleav [91] to predict dicer processing sites using Support Vector Machine (SVM) models and RISCbinder [92] to predict the guide strand of miRNAs. Finally, authors analyzed the allele frequencies data of SNPs from the HapMap [79] populations to evaluate the possible effect of the variations in the miRNA and its penetrance in the population.

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Computational knowledge is often the result of an integration between highly reliable sources and intermediated steps of computational analyses. Pubmed articles and manually curated databases storing for example sequences and their related annotations, such as miRBase, are mined to support computational procedures, like algorithms used in predicting target binding. There exist well-established tools for modeling different aspects regarding miRNAs, here we focus on those aiming at assess functional effects of somatic mutations in miRNAs. mir2GO [93] is one of the most used tools in analyzing miRNA mutations and their functional impact. It is a modular web-based platform on top of which two different workflows, miRmut2GO and miRpair2GO, provide miRNA mutation analysis by means of GO (Gene Ontology) enrichment analysis. miRmut2GO investigates functional effects of genetic and somatic mutations in miRNA seed regions, and miRpair2GO is a functional analysis tool that compares two miRNAs by looking at their target genes. Starting from the fact that the main regulatory signal, namely target miRNA target recognition, is primarily encoded in seed regions, both workflows rely on the same core methodology that executes and enrichment functional analysis on reference and derived target genes, obtained through target prediction algorithms. Finally, a GO graph is used to visualize the functional effects of miRNA seed mutations. In [94], authors proposed miRNA SNiPer, an online tool for the detection of miRNA polymorphisms in vertebrates. The system, which focuses on variants in the miRNA seed, allows to access to a catalog of miRNA seed region polymorphisms (miR-seed-SNPs) consisting of 149 SNPs in six species. Interestingly, authors determined that miR-SNPs were frequently located within the QTL, chromosome fragile sites, and cancer susceptibility loci, indicating their potential role in the genetic control of various complex traits. The ImiRP [95] (Illegitimate miRNA Predictor) computational tool is a system aiming at study possible mutations affecting miRNA-mRNA interactions. A random generator is used to mutate 3′UTR mRNA sequences that are targets of specific miRNAs. The methodology applies a specific mutation strategy such that target site mutations do not inadvertently create new miRNA target sites. Illegitimate mutated target sites are identified by comparing target site predictions between input and mutant sequences on top of the miRBase high confidence knowledge. The creation of unwanted target sites may affect the experimental outcome, in fact, a common approach to validate and probe miRNA–mRNA interactions is to mutate predicted target sites within the mRNA and determine whether it affects miRNA-mediated activity. The software is able to investigate multiple miRNA target sites at once and it can be useful to study miRNA cooperativity in case of multiple mutagenesis. The core of the system is a Java-based application, such that it can be used in user-defined computational pipelines, and a web-based interface is provided, too. The mrSNP [96] software was designed for similar intents, however it does not have the capacity of generate random mutations, that must be provided by the user. In ImiRP, target mutation is a task performed efficiently and with ad hoc strategies. In [97] authors introduced a computational tool to predict SNP pathological effects on miRNAbased gene regulation. It can help to identify SNPs causative to diseases, such as cancer. The system focuses on SNPs that may affect miRNA targeting and thereby cause gene dysregulation. The tool predicts the effects of SNPs in miRNA target sites and uses linkage disequilibrium to map those mirSNPs to SNPs of interest in GWAS. Authors showed that the predictions correspond well to the SNP’s measured effects on miRNA regulation.

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CONCLUSION miRNA genes’ variations may have important effects at different functional levels in miRNAs. These include the transcription of miRNAs, maturation, and targeting. The miRNA transcripts need to meet several prerequisites to be able to express the mature miRNA sequences. The variability in miRNA genes can therefore influence both the expression level of the corresponding mature miRNA and give rise to a differential regulation of target genes. This, may impact biological processes by affecting the posttranscriptional regulation of the target genes and more in general cause biological pathways perturbation. Consequently, SNPs in some miRNAs may lead to various diseases such as cancers. This chapter recounts the biogenesis of miRNAs and they functional roles in normal and pathological conditions. It focuses on genetic alterations and their system level consequences that determine phenotypes. Reader is introduced to both the biomedical descriptions of the phenomena and the computational tools essential for its analysis and characterization.

LIST OF ACRONYMS AND ABBREVIATIONS 3′-UTR 3′-untranslated region ceRNA  Competing endogenous RNA circRNA  circularRNA IRAK1  Interleukin 1 Receptor Associated Kinase 1 isomiR  isomiRNA KRAS  Kirsten rat sarcoma viral oncogene homolog lncRNA  Long noncoding RNA miRNA  MicroRNA mRNA  Messenger RNA pre-miR  Precursor miRNA pri-miR  Primary miRNA PTC1  Patched gene 1 RISC  RNA-induced silencing complex SLITRK1  SLIT and NTRK-like family, member 1 TRAF6  TNF Receptor Associated Factor 6

GLOSSARY Circular RNA (circRNA)  A type of RNA which forms a covalently closed continuous loop. Competing endogenous RNAs (ceRNAs)  Molecular sponges for miRNAs. This type RNAs regulate RNA transcripts by competing for shared targeted miRNAs. Flanking region  A region of a gene preceding or following the transcribed region. These regions primarily function in the regulation of gene transcription. The 5′-flanking region contains the promoter and may contain enhancers or other protein binding sites. isomiR  An RNA sequence that have variations, due to additions, deletions or substitutions, with respect to the reference miRNA sequences. Long noncoding RNA (lncRNA)  Nonprotein coding transcript longer than 200 nucleotides. MiRISC complex  A RISC (RNA-induce silencing complex) that incorporates a miRNAs.

References

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MicroRNA (miRNA)  A small endogenous noncoding RNA molecules, which sequence is about 22 nucleotides long, that regulates gene expression at posttranscriptional level and functions in RNA silencing. MiRNA gene  A gene from which a miRNA is transcribed. MiRNAs may be transcribed from either introgenic regions, from within introns of other genes such as other noncoding RNA and also from exons of coding genes. miRNA targeting  miRNAs regulate gene expression by targeting a repressor complex to specific messenger RNAs. Pathway  A series of actions among molecules in a cell that leads to a certain product or a change in a cell. Pre-miR  A precursor miRNA is the result of cleaving the hairpin of a pri-miRNA in the nucleus by Drosha (double-strand-specific ribonuclease). It is transported to the cytoplasm via a process involving Exportin-5. A pre-miRNA is further cleaved by Dicer to generate a short RNA in which one strand is the mature miRNA. pri-miR  A primary miRNA transcript directly transcribed in the nucleus from a DNA region. It can be more than 1000 nucleotides long and contains an RNA hairpin in which one of the two strands includes the mature miRNA. Seed  A miRNA conserved heptametrical sequence within 2–7 nucleotides from 5′-end that mediates target recognition through base pairing complementary. Single-nucleotide polymorphism (SNP)  A variation in a single nucleotide of a genetic sequence such that is has an appreciable degree of presence within a given population.

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