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SNPs in microRNA target sites and their potential role in human disease

Adrianna Moszyńska, Magdalena Gebert, James F. Collawn and 1 more

Open Biology · 2017 · Review

doi:10.1098/rsob.170019

Abstract

In the post-genomic era, the goal of personalized medicine is to determine the correlation between genotype and phenotype. Developing high-throughput genotyping technologies such as genome-wide association studies (GWAS) and the 1000 Genomes Project ( http://www.internationalgenome.org/about/#1000G_PROJECT ) has dramatically enhanced our ability to map where changes in the genome occur on a population level by identifying millions of single nucleotide polymorphisms (SNPs). Polymorphisms, particularly those within the coding regions of proteins and at splice junctions, have received the most attention, but it is also now clear that polymorphisms in the non-coding regions are important. In these non-coding regions, the enhancer and promoter regions have received the most attention, whereas the 3′-UTR regions have until recently been overlooked. In this review, we examine how SNPs affect microRNA-binding sites in these regions, and how mRNA stability changes can lead to disease pathogenesis.

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 22, 2026. Paragraph numbers are ours, so an answer can point at one.

Introduction

Single nucleotide polymorphisms (SNPs) occur in 1% or more within the population. Although these populations are identical in 99.5% at the DNA level, there are approximately 10 million SNPs in the human genome, indicating that they occur once in every 300 bp in both coding and non-coding regions of genes. SNPs in the coding region can result in synonymous and non-synonymous changes, with the latter resulting in an amino acid change or the introduction of a stop codon. These changes can lead to human diseases, and in fact at least 25% of the reported non-synonymous SNPs are predicted to negatively affect protein function.

Synonymous SNPs have been referred to as silent mutations because they do not change the amino acid. However, there is a growing body of evidence indicating that synonymous SNPs do affect proper protein function. For example, two synonymous SNPs in the sequence encoding the multidrug resistance protein 1 (MDR1) affect protein folding and function. Moreover, the most common disease-causing mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene is an out-of-frame deletion of phenylalanine-508 (ΔF508) that introduces a SNP at isoleucine-507 (I507) and this SNP contributes to the severity of the ΔF508 CFTR channel dysfunction.

Recently, more attention has been paid to the SNPs identified in non-coding regions. Interestingly, about 93% of functional SNPs in the GWAS catalogue are in non-coding regions. They have been called regulatory SNPs (rSNPs) because they affect transcriptional regulation or post-transcriptional gene expression. rSNPs can cause changes in cell function at different levels of gene regulation. For example, they can affect gene splicing and transcription factor binding. These rSNPs reside in the sequence of non-coding RNA in the promoter and enhancer regions. They can also affect the half-life of messenger RNA (mRNA) and result in decreased protein levels through mRNA–microRNA (miRNA) interactions. SNPs in miRNA target sites in the 3′-UTR of mRNAs are referred to as poly-miRTSs. The SNP dataset from the UCSC Genome browser (NCBI dbSNP, Build 130) consists of 18 833 531 human SNPs, while the genomic coordinates for a subset of 175 351 (approx. 11%) locates them in the 3'-UTRs of 16 810 genes. Given that there are an estimated 19 000–20 000 genes in the human genome, this suggests that the majority of the genes could be regulated by miRNAs, indicating that the potential biological consequence of these mutations should be carefully considered. Furthermore, a substantial number of SNPs and rare mutations within pri-, pre- and mature miRNA sequences have been reported. Although some of these miRNA SNPs are related to human diseases (reviewed in), their biological role is difficult to elucidate given that changes in any miRNA can have profound genome-wide effects since miRNAs can bind to hundreds of different mRNAs. Since 2008, when Sethupathy & Collins critically reviewed reports of miRNA SNPs involved in human diseases and provided clear criteria for validation of such associations, a large number of novel human disease-related poly-miRTSs have been proposed. Furthermore, recently developed approaches dedicated to miRNA function, targeted genome editing with in silico methods provide novel tools for complex verification of miRNA SNP consequences. In this review, we focus on poly-miRTSs and their potential impact in human diseases.

mRNA: miRNA association

miRNAs are short (approx. 22 nt) endogenous non-coding single-stranded RNAs which act as post-transcriptional regulators of gene expression. In the cytosol, mature miRNAs that are a part of the Argonaute-containing silencing complexes called miRNA ribonucleoprotein complexes (miRNP) can downregulate a specific target mRNA by Argonaute-catalysed degradation of the mRNA target strand in P bodies or by translational repression. Hence, the major consequence of miRNA: mRNA pairing is loss of protein expression, resulting from either decreased transcript levels or by translational repression.

Although the mechanism underlying the recognition of mRNA targets by miRNAs has been extensively studied, the minimal requirements for a functional mRNA: miRNA association are not fully understood. Furthermore, despite the fact that many mRNAs have conserved miRNA target sites, a variety of interactions through non-conserved sites has been reported. Finally, the average size of the human 3′-UTR is about 950 nt (for highly expressed neuronal genes it is 1300 nt, whereas for genes specific to non-neuronal tissue it is only 700 nt), while the efficient miRNA-binding site consists of 6–8 nt. Hence, the 3′-UTR of a specific mRNA can include tandem target sequences for a specific miRNA as well as target sequences for many other miRNAs. The composition of specific miRNAs associated with the 3′-UTR of a mRNA along with the efficiency of miRNA pairing to their target sequences impacts the mRNA's half-life and influences protein levels. Hence, determining the consequences of SNPs in miRNA target sites is not a trivial endeavour.

That being said, it is well established that the complementary pairing of a 3′-UTR of a mRNA to a conserved heptametrical seed sequence is usually found at positions 2–7 from the miRNA 5′-end and is critical for mRNA target selection. Initially, it was thought that perfect complementarity of the 3′-UTR of a mRNA to the miRNA seed sequence led to transcript degradation, and a partial match caused translational inhibition. However, recent studies have shown that non-canonical sites also exist and can regulate mRNA degradation. Furthermore, base pairing between mRNA and miRNA seed sequences do not always lead to decreased expression of target transcript. The above findings suggest that additional features of mRNA target sequences play a crucial role in effective miRNA binding. The detailed analysis of seed sequences established 8-nt pairing (8-mer) with mRNA as the most effective, whereas 7- and 6-nt binding sites (7-mer and 6-mer) were less effective (figure 1). Although 6-mers often provide efficient pairing, even in an offset position (figure 1a,b), a 4-mer is a non-functional site in vivo. Interestingly, 7-mer pairing efficiency relies strictly on sequence complementarity. Consequently, although the 7-mer-m8 site (an exact match to positions 2–8 of the mature miRNA—the seed and position 8 (figure 1c)) has increased seed pairing compared with the 6-mer, the 7-mer-A1 (an exact match to positions 2–7 of the mature miRNA—the seed followed by an ‘A1’) has similar seed pairing to 6-mer (figure 1d). The seed pairing including both the match at position m8 and the A1 is characteristic for a 8-mer site (figure 1e). The effect of G: U base pairs and bulges in the seed were also considered showing that a single G: U wobble or target sites with a 1 nt bulge can still be functional (figure 1f). However, the Watson–Crick base pairing is absolutely critical between nucleotides at positions 9–12 in the target site, since the hydrolysis of the phosphodiester backbone in mRNA cleaved by miRNA occurs only when the 10th and 11th nucleotides of mRNA are complementary to nucleotides at positions 2–15 in miRNA.

Furthermore, additional mRNA pairing to the 3′ region of miRNA, termed as productive seed pairing, can increase the target recognition or it can compensate for the mismatch to the seed (3′ supplementary sites and 3′ compensatory sites, respectively). The substantial pairing of 3′ compensatory sites to mRNA increases the weak 5′ pairing, resulting in functional miRNA binding (figure 1g,h).

Interestingly, Shin et al. indicated that centred mRNA sequences consisting of 11 nt create Watson–Crick pairs with miRNA nucleotides at positions 4–14 or 5–15 and serve as a type of miRNA target site. This unique class of miRNA target sites is devoid of both perfect seed pairing and 3′ compensatory pairing but can be supplemented by pairing to the other miRNA areas (figure 1i).

Based on the studies discussed above, mRNA target sites can be divided into two major groups. The first group consists of canonical sites with (i) strong seed pairing to the 5′ end of miRNA (low pairing energy) that is amplified through either strong base pairing to the 3′ end of the miRNA (an extension of the seed type) or (ii) strong seed pairing to the 5′ end of miRNA seed sites requiring little or no 3′-UTR pairing support. These canonical sites have pairing energy and are often functional in one copy. In contrast with these sites, the second groups are non-canonical seed sites with higher pairing energy that should exist in the 3′-UTR in more than one copy to be effective. It has to be stressed that the seed region contributes the majority of binding energy and strong binding relies mainly on base pairing within this region, whereas an additional 3′ pairing only slightly reduces binding energy. Interestingly, pairing beyond position 16 and at positions 10–11 increases binding energy that results in weakened binding.

Another factor to consider in miRNA: mRNA interactions is the location of the target mRNA sites. In general, the 3′-UTR mRNA sites are most efficient. Furthermore, target mRNA sites positioned within at least 15 nt from the stop codon, sites located away from the centres of long 3′-UTR, as well as those miRNA target sites located in AU-rich regions are the most effective. Additionally, a location of target mRNA sites in close proximity to protein-binding sites and to other miRNA-binding sites may also affect miRNA: mRNA associations. The 3′-UTR mRNA quartiles bordering the mRNA poly(A) tail and the ORF exhibit more effective targeting than remaining two centred quartiles. However, this effect was apparent only for longer 3′-UTRs (more than 1300 nt).

Taking into account the complexity of miRNA: mRNA pairing, the introduction of a SNP into a 3′-UTR can have numerous functional consequences by either introducing or removing miRNA target sequences or changing the binding efficiency. The poly-miRTSs within the canonical seed sequence can either create a novel mRNA target site from a preexisting 5-mer sequence (into 6-mer offset or 6-mer) or impair the existing target site 6-mer or 6-mer offset sequence (into 5-mer). Furthermore, since the introduction of poly-miRTSs into seed regions can also affect miRNA: mRNA binding efficiency, it can lead to either increased or decreased post-transcriptional mRNA regulation. Finally, poly-miRTSs may also affect miRNA-binding efficiency by changing supplemental seed pairing that applies to both canonical and non-canonical binding sites. Additionally, in the case of non-canonical binding sites, poly-miRTSs may introduce or remove tandem target sites, and thus change the miRNA effects. Finally, the introduction or removal of miRNA target sites may affect binding to other miRNA target sequences in the SNP's close proximity, which could have unforeseen effects on the mRNA half-life. Given the number of SNPs in the human population, it is not surprising that poly-miRTSs have been shown to affect the levels of numerous proteins that have been associated with various disorders (table 1). Below, we discuss examples of several studies identifying poly-miRTSs and their potential association with human disorders.

MDM4 | miR-191 or miR-877-3p

Mdm2-like p53-binding protein (MDM4) is an oncoprotein that negatively regulates the p53 tumour suppressor protein. It is well documented that overexpression of this protein leads to cancer development. Recent studies suggested that the variation in the 3'-UTR of MDM4 can lead to a decreased risk of various malignancies. The occurrence of the C minor allele (SNP rs4245739 A>C) in the 3'-UTR of MDM4 has been shown to decrease the risk of cancer, and delay the progression of metastasis and cancer-related death. Numerous studies have shown that introduction of this C minor SNP creates a new binding site for miR-191 and/or miR-887-3p, and this leads to a decreased level of MDM4 protein. Moreover, a recently conducted meta-analysis of 69 477 subjects (19 796 cases of nine various type of cancer and 49 681 controls) showed that the above-mentioned SNP correlates with a reduced overall risk of cancer.

ΔNp63 | miR-140-5p

p63 is another tumour suppressor protein belonging to the p53 family. Because of different promoters and alternative splicing, there are two major isoforms of TP63: TAp63 (acidic transactivation domain present) and ΔNp63 (no transactivation domain). Interestingly, in vivo experiments indicate that TAp63 acts like a tumour suppressor gene, whereas ΔNp63 is an oncogene. Wang et al. found that the SNP rs35592567 (C>T) in the 3′-UTR of ΔNp63 has an impact on bladder cancer risk. Analysis showed that the T allele is correlated with a decreased risk of bladder cancer because miR-140-5p is able to bind to the 3'-UTR of ΔNp63. Overexpression of miR-140-5p in 5637 cells (urinary bladder grade II carcinoma cells) attenuated cell migration and invasion and inhibited cell proliferation.

HNF1B | miR-214-5p and miR-550a-5p

Another example of a positive effect of a SNP on a disease risk is rs2229295 (C>A), which is located in the 3′-UTR of hepatocyte nuclear factor 1-beta (HNF1B) mRNA. This gene encodes a transcription factor known to be a regulator of growth and development in the pancreas. Since HNF1B has a role in controlling hepatic insulin activity and glucose metabolism in vivo, Goda et al. suggested that the rs2229295 SNP may correlate with susceptibility for type 2 diabetes mellitus (T2DM). Using luciferase reporter vectors, they demonstrated that the A allele constructs were regulated by two miRNAs: miR-214-5p and miR-550a-5p, whereas C allele constructs were not. Hence, the presence of A allele decreases HNF1B protein levels and has a protective effect against T2DM.

APOC3 and APOA5 | miR-4271 and miR-485-5p

APOC3 and APOA5 are genes that encode apolipoprotein C3 and A5, respectively. Both of these proteins along with lipoprotein lipase (LPL) and apolipoprotein C2 (APOC2) are involved in triglyceride metabolism. Hu et al. demonstrated that decreased levels of APOC3 lead to lower triglyceride levels and reduce the risk of coronary heart disease (CHD). This is due to SNP (rs4225 G>T) found in the 3′-UTR of APOC3. When the T minor allele is present in the cell, miR-4271 is able to bind to the 3′-UTR of APOC3, and this leads to a decreased translation of APOC3. miR-4271, however, cannot bind to the variant containing the G major allele. Similarly, APOA5 c.*158C>T (rs2266788) is also associated with alterations in triglyceride metabolism and results in hypertriglyceridaemia. In this case, the rare c.*158C APOA5 allele creates a new functional binding site for miR-485-5p. Importantly, both miRNAs regulating APOC3 and APOA5 are endogenously expressed in the human liver, so if the SNP occurs, they may be involved in the regulation of triglyceride metabolism in vivo. However, both examples of SNPs and their impact on the risk of disease need further clarification, since different results have been obtained for different ethnic groups.

PLIN4 | miR-522

PLIN4 (perilipin 4) is a member of the perilipin family and these proteins coat the intracellular lipid storage droplets (LSD). PLIN4 has been proposed to promote uptake of free fatty acids from the blood to the LSD and is dependent upon the cell's nutritional status. Meta-analysis of two populations of this gene, rs8887 (G>A), analysed with antropometric measurements, indicated that the two populations were different. Individuals with the A minor allele had an increased volume of visceral and subcutaneous adipose tissue, and higher BMI and weight compared to individuals with the G major allele. This study reported that PLIN4 is regulated by miR-522 only in the rs8887A variant. It is not yet clear, however, if the lower expression of PLIN4 contributes to obesity because the results are conflicting.

FXN | miR-124-3p

Reduced expression of the mitochondrial frataxin (FXN) protein has been postulated to play a role in Friedreich's ataxia (FRDA), an inherited neurodegenerative disease. Lower levels of frataxin are due to GAA repeat expansion in the FXN gene. Additionally, Bandiera et al. have suggested that miR-124-3p regulates FXN expression in vivo only in FRDA patients. They identified seven SNPs in the 3′-UTR of FXN in children and adults diagnosed with FRDA. One of them, rs11145043 (G>T), permits miR-124-3p binding only when the T allele is present. Although miR-124-3p is highly expressed in the nervous system, it is overexpressed in FRDA patients, suggesting its role in FRDA. However, its influence on FXN needs further clarification.

SCNA | miR-34b

The α-synuclein SCNA gene polymorphism is considered a main risk for the common sporadic form of Parkinson's disease (PD; approx. 90% of all PD cases). α-Synuclein is a crucial protein that creates immunoreactive aggregates in Lewy-bodies, which are typical for Parkinson's disease patients' brains. Studies have indicated that miR-34b targets the α-synuclein mRNA3′-UTR in two distinct sites and represses translation of this protein. Importantly, in PD patients' brains, the level of miR-34b in the substantia nigra is decreased. Kabaria et al. have identified a SNP, rs10024743 (T>G), in the 3′-UTR of α-synuclein, which is localized in the target site 1 of miR-34b. This SNP diminishes the miR-34b-mediated repression of α-synuclein levels due to disruption of the miRNA: mRNA association. However, this study was performed only on SH-SY5Y cells and its association with PD remains unclear.

PALLD | miR-96 and miR-182

The PALLD gene encodes the actin-associated protein Palladin, whose expression correlates closely with the pathological cell motility characteristics of aggressive cancer cells. The expression level of Palladin in breast cancer patients is higher in invasive and malignant cancer cell types than in non-invasive and normal cell lines. The results suggest that Palladin promotes podosome formation, regulates the actin cytoskeleton via multiple pathways, participates in matrix degradation, and thus facilitates metastasis in breast cancer. Gilam et al. have reported that miR-96 and miR-182 reduce breast cancer cell migration and invasion by downregulating Palladin protein levels and that this process is disrupted by a SNP, rs1071738 (G < C), located in the 3′-UTR of the PALLD gene. This SNP is characterized by highest minor allele frequency (greater than 43%) and the alternate G allele is much more common than the ancestral minor C allele. If the C allele occurs in the binding site, the mRNA target sequence at the 3′-UTR of PALLD is fully complementary to the miR-96 and miR-182 seed regions, whereas the presence of the alternate G allele results in one mismatch. A significant decrease in Palladin levels is diminished by miR-96 and miR-182 expression (approx. 30% and approx. 70% reduction, respectively) in the presence of the C allele, but not in the presence of the G allele due to the disrupted miRNA:mRNA association. These findings suggest that although miR-96 and miR-182 may prevent breast cancer metastasis, the functional rs1071738 G variant abolishes this effect.

EFNB2 | miR-137

The EFNB2 (ephrin-B2) gene encodes an ephrin, a protein tyrosine kinase that is involved in remodelling and the development of synaptic connections that are regulated by activated NMDA receptor. Ephrin-B2 is essential for the Reelin pathway controlling neuronal migration. Additionally, the activation of EFNB2 is crucial for rescuing the Reelin defect and disruption of this pathway is associated with schizophrenia. Recently, a negative correlation between miR-137 and EFNB2 expression was shown. Importantly, the SNP rs550067317 (A>C) is located at the predicted target site of miR-137 in the 3′-UTR of EFNB2. The minor C allele of rs550067317 disrupts the formation of the typical stem-loop structure during base pairing of miR-137 with the predicted target sequence at the 3′-UTR, consequently reversing inhibition of EFNB2 expression.

HIF1A | miR-199a

The HIF1A gene encodes the HIF-1α protein (hypoxia-inducible factor 1), an oxygen dependent subunit and master transcriptional regulator of the mammalian cell response to oxygen deprivation, and is therefore important in both the cardiovascular and cancer fields. To date, numerous studies have demonstrated miRNA's role in regulation of HIF-1α levels. Recently, a SNP (rs2057482 T>C) in the 3′-UTR of HIF1A located near the miR-199a binding site was identified. The C allele of this variant has an increased frequency in pancreatic ductal adenocarcinoma patients and this CC genotype was characterized by a larger tumour size, shorter overall survival and a higher risk of this disease compared to CT and TT genotypes. Additionally, the occurrence of the C allele was significantly associated with higher HIF1A mRNA and consequently upregulation of HIF1 levels, suggesting that this SNP impairs miR-199a: HIF1A binding.

DROSHA | miR-27b

A very interesting example of a synonymous mutation that leads to the loss of an miRNA binding site is SNP rs10719 (T>C) located in the 3′-UTR of the DROSHA gene. The Drosha enzyme, a member of the RNAase III family, plays a critical role in miRNA biogenesis. It liberates the pre-miRNA stem-loop by cleavage of the longer pri-miRNAs in the nucleus. In addition to this function, Drosha also influences cell proliferation and apoptosis. Since overexpression of Drosha is observed in bladder cancer, this SNP is associated with an increased risk of bladder cancer. Yuan et al. reported that DROSHA's 3′-UTR contains a target site for miR-27b, while rs10719 (T>C) is located in close proximity to this site (46 bp downstream of the miR-27b binding site). They have postulated that rs10719T to C transition leads to weaker mRNA: miRNA association at the miR-27b target site and consequently to increased Drosha expression.

FGF20 | miR-433

An example of another poly-miRTS related to PD was provided by Wang et al., who reported a correlation between SNP (rs127202208 C/T) in the 3′-UTR of fibroblast growth factor 20 (FGF20) and the development of PD. FGF20 is mainly expressed in substantia nigra and increases proliferation and promotes survival of dopaminergic neurons during the early stages of life. However, increased levels of FGF20 in the later stages of life enhance α-synuclein expression and can lead to the death of dopaminergic neurons. miR-433, which is abundant in brain, was shown to downregulate the translation of FGF20, mainly because this reported SNP resides within the predicted binding site for miR-433. The allele C of this polymorphism represents a valid miRNA base pairing, whereas the T allele introduces a G: U wobble base pairing and consequently a mismatch, which affects the miRNA: mRNA interaction. However, this SNP does not eliminate the miRNA: mRNA binding, but attenuates it. This leads to increased FGF20 levels and indirectly to overexpression of α-synuclein. Importantly, the effect of this SNP on FGF20 expression and its relationship to miR-433 levels were confirmed in vivo.

Conclusion

The discussed examples of poly-miRTSs strongly suggest that these SNPs can be crucial factors in developing human pathologies and could contribute to genetic diversity. As mentioned, roughly 180 000 SNPs in the human genome that are located in the 3'-UTR region were identified along with about 2600 mature miRNA sequences which are deposited in the mirBase (v. 21), suggesting that a large number of these SNPs may introduce miRNA-binding changes. Furthermore, the recent development of deep sequencing techniques and advanced database/software tools like miRSNP and PolymiRTS Database 3.0 (see table 2 for complete list) allows researchers to initially access potential poly-miRTSs. Hence, in the near future, we can expect growing numbers of studies linking poly-miRTSs to human diseases.

In 2008, Sethupathy & Collins provided criteria for assigning SNPs as poly-miRTSs involved in human diseases that include: (i) functional (preferably in vivo) experimental validation of SNPs related to differential mRNA targeting; (ii) genetic testing of the association with the disease that takes into account the effects of population stratification; and finally (iii) mechanistic testing underlying the mechanism by which poly-miRTSs contribute to the disease. Few current studies satisfy all these criteria (table 1), while the majority of them rely on population correlation effects and in silico modelling only, ignoring the necessity of the mechanistic approach. Importantly, commonly used methods to confirm differential miRNA: mRNA binding, in vitro luciferase reporter constructs and miRNA overexpression often do not consider the physiological miRNA levels in vivo. However, miRNA physiological levels are often undergoing dynamic changes due to epigenetic factors, and thus they can affect the verification of the poly-miRTS disease-related mechanisms. The luciferase-based reporter assays are usually performed in artificial cancer cell lines that permit easy AgoMiR (mimic) delivery, and are often characterized by low endogenous miRNA levels. The latter inhibits endogenous miRNAs from degrading the reporters prior to the miRNA overexpression. Importantly, the miRNA overexpression in these systems is often a hundred fold higher than in vivo conditions. Hence, in the case of validation of new target sites created by poly-miRTS, this experimental model may lead to false positive results, since it cannot differentiate between weak and strong binding to the targets. The vector-based miRNA expression system that provides inducible and scalable control over miRNA levels may provide more solid verification of potential miRNA: mRNA binding.

Recently, the development of morpholino-based target protector technology provides an elegant tool to test the functionality of novel potential miRNA: mRNA interactions that mimics physiological conditions. Target protectors bind to specific target mRNA sequences and block miRNA access, however without triggering an RNAi response. Hence, target protectors allow blocking the miRNA-mediated suppression of a specific target mRNA. Importantly, these modified oligonucleotides can be used to evaluate the significance of miRNA: mRNA interactions in the context of physiological miRNA levels.

Furthermore, often changes in a gene's mRNA level are not reflected in its protein levels. Hence, the studies of miRNA SNP-affected targets should be always accompanied by monitoring protein levels in cell lines related to the disease. Finally, although in research models usually one miRNA and one target are considered, the single miRNA usually is predicted to bind hundreds of target mRNAs, and have multiple effects on cellular metabolism. Hence, studying the mechanism of poly-miRTS involvement in human diseases requires verification that the miRNA effects are a result of indirect disease-related targets. Although this possibility cannot be totally eliminated, following genome-wide effects of specific miRNA modulation (with next generation sequencing) can support direct miRNA: mRNA interactions.

The most convincing and final criterion for linking poly-miRTSs to disease is establishing the disease-related mechanisms of differential miRNA binding. Taking into account complexity of a potential SNP effect on miRNA: mRNA pairing, this can be challenging. Nevertheless, the recent development of targeted genome editing tools (like CRISPR/Cas9 systems) allows one to make efficient, precise and targeted changes to the genome of the living cells, and opens novel possibilities to overcome this limitation. Sadly, to date, no study has been reported in which targeted genome editing was applied in order to validate poly-miRTSs.

Analysing the specific effects of homozygotic and heterozygotic SNPs in both in vitro and in vivo disease models could provide the critical proof for the role of and frequency that poly-miRTSs occur in human diseases.

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    Mullany LE, Herrick JS, Wolff RK, Slattery ML 2016 Single nucleotide polymorphisms within microRNAs, microRNA targets, and microRNA biogenesis genes and their impact on colorectal cancer survival. Genes Chromosomes Cancer 56, 285–295. (doi:10.1002/gcc.22434)

    Not in our library10.1002/gcc.22434

  25. 25.

    Liu X, Han Z, Yang C In press. Associations of microRNA single nucleotide polymorphisms and disease risk and pathophysiology. Clin. Genet. (doi:10.1111/cge.12950)

    Not in our library10.1111/cge.12950

  26. 26.

    Kim J, Choi GH, Ko KH, Kim JO, Oh SH, Park YS, Kim OJ, Kim NK 2016 Association of the single nucleotide polymorphisms in microRNAs 130b, 200b, and 495 with ischemic stroke susceptibility and post-stroke mortality. PLoS ONE 11, e0162519 (doi:10.1371/journal.pone.0162519)

    Not in our library10.1371/journal.pone.0162519

  27. 27.

    Morales S, Gulppi F, Gonzalez-Hormazabal P, Fernandez-Ramires R, Bravo T, Reyes JM, Gomez F, Waugh E, Jara L 2016 Association of single nucleotide polymorphisms in Pre-miR-27a, Pre-miR-196a2, Pre-miR-423, miR-608 and Pre-miR-618 with breast cancer susceptibility in a South American population. BMC Genet. 17, 109 (doi:10.1186/s12863-016-0415-0)

    Not in our library10.1186/s12863-016-0415-0

  28. 28.

    Bartel DP 2004 MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297. (doi:10.1016/S0092-8674(04)00045-5)

    Not in our library10.1016/s0092-8674(04)00045-5

  29. 29.

    Winter J, Jung S, Keller S, Gregory RI, Diederichs S 2009 Many roads to maturity: microRNA biogenesis pathways and their regulation. Nat. Cell Biol. 11, 228–234. (doi:10.1038/ncb0309-228)

    Not in our library10.1038/ncb0309-228

  30. 30.

    Shin C, Nam J-W, Farh KK-H, Chiang HR, Shkumatava A, Bartel DP 2010 Expanding the microRNA targeting code: functional sites with centered pairing. Mol. Cell 38, 789–802. (doi:10.1016/j.molcel.2010.06.005)

    Not in our library10.1016/j.molcel.2010.06.005

  31. 31.

    Lewis BP, Burge CB, Bartel DP 2005 Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20. (doi:10.1016/j.cell.2004.12.035)

    Not in our library10.1016/j.cell.2004.12.035

  32. 32.

    Sood P, Krek A, Zavolan M, Macino G, Rajewsky N 2006 Cell-type-specific signatures of microRNAs on target mRNA expression. Proc. Natl Acad. Sci. USA 103, 2746–2751. (doi:10.1073/pnas.0511045103)

    Not in our library10.1073/pnas.0511045103

  33. 33.

    Bartel DP 2009 MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233. (doi:10.1016/j.cell.2009.01.002)

    Not in our library10.1016/j.cell.2009.01.002

  34. 34.

    Filipowicz W, Bhattacharyya SN, Sonenberg N 2008 Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat. Rev. Genet. 9, 102–114. (doi:10.1038/nrg2290)

    Not in our library10.1038/nrg2290

  35. 35.

    Esquela-Kerscher A, Slack FJ 2006 Oncomirs—microRNAs with a role in cancer. Nat. Rev. Cancer 6, 259–269. (doi:10.1038/nrc1840)

    Not in our library10.1038/nrc1840

  36. 36.
  37. 37.

    Grimson A, Farh KK-H, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP 2007 MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol. Cell 27, 91–105. (doi:10.1016/j.molcel.2007.06.017)

    Not in our library10.1016/j.molcel.2007.06.017

  38. 38.
  39. 39.

    Salzman DW, Weidhaas JB 2013 SNPing cancer in the bud: microRNA and microRNA-target site polymorphisms as diagnostic and prognostic biomarkers in cancer. Pharmacol. Ther. 137, 55–63. (doi:10.1016/j.pharmthera.2012.08.016)

    Not in our library10.1016/j.pharmthera.2012.08.016

  40. 40.

    Jens M, Rajewsky N 2015 Competition between target sites of regulators shapes post-transcriptional gene regulation. Nat. Rev. Genet. 16, 113–126. (doi:10.1038/nrg3853)

    Not in our library10.1038/nrg3853

  41. 41.

    Garcia DM, Baek D, Shin C, Bell GW, Grimson A, Bartel DP 2011 Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat. Struct. Mol. Biol. 18, 1139–1146. (doi:10.1038/nsmb.2115)

    Not in our library10.1038/nsmb.2115

  42. 42.

    Gao F, Xiong X, Pan W, Yang X, Zhou C, Yuan Q, Zhou L, Yan M 2015 A regulatory MDM4 genetic variant locating in the binding sequence of multiple microRNAs contributes to susceptibility of small cell lung cancer. PLoS ONE 10, e0135647 (doi:10.1371/journal.pone.0135647)

    Not in our library10.1371/journal.pone.0135647

  43. 43.

    Stegeman S, Moya L, Selth LA, Spurdle AB, Clements JA, Batra J 2015 A genetic variant of MDM4 influences regulation by multiple microRNAs in prostate cancer. Endocr. Relat. Cancer 22, 265–276. (doi:10.1530/ERC-15-0013)

    Not in our library10.1530/erc-15-0013

  44. 44.

    Wynendaele J et al. 2010 An illegitimate microRNA target site within the 3' UTR of MDM4 affects ovarian cancer progression and chemosensitivity. Cancer Res. 70, 9641–9649. (doi:10.1158/0008-5472.CAN-10-0527)

    Not in our library10.1158/0008-5472.can-10-0527

  45. 45.

    Fan C, Wei J, Yuan C, Wang X, Jiang C, Zhou C, Yang M, Krahe R. 2014 The functional TP53 rs1042522 and MDM4 rs4245739 genetic variants contribute to non-Hodgkin lymphoma risk. PLoS ONE 9, e107047 (doi:10.1371/journal.pone.0107047)

    Not in our library10.1371/journal.pone.0107047

  46. 46.

    Zhou L et al. 2013 Association of a genetic variation in a miR-191 binding site in MDM4 with risk of esophageal squamous cell carcinoma. PLoS ONE 8, e64331 (doi:10.1371/journal.pone.0064331)

    Not in our library10.1371/journal.pone.0064331

  47. 47.

    Yang Y, Gao W, Ding X, Xu W, Liu D, Su B, Sun Y 2017 Variations within 3'-UTR of MDM4 gene contribute to clinical outcomes of advanced non-small cell lung cancer patients following platinum-based chemotherapy. Oncotarget 8, 16 313–16 324. (doi:10.18632/oncotarget.10771)

    Not in our library10.18632/oncotarget.10771

  48. 48.

    Wang M et al. 2016 A functional variant in TP63 at 3q28 associated with bladder cancer risk by creating an miR-140-5p binding site. Int. J. Cancer 139, 65–74. (doi:10.1002/ijc.29978)

    Not in our library10.1002/ijc.29978

  49. 49.

    Goda N, Murase H, Kasezawa N, Goda T, Yamakawa-Kobayashi K 2015 Polymorphism in microRNA-binding site in HNF1B influences the susceptibility of type 2 diabetes mellitus: a population based case-control study. BMC Med. Genet. 16, 75 (doi:10.1186/s12881-015-0219-5)

    Not in our library10.1186/s12881-015-0219-5

  50. 50.

    Hu SL, Cui GL, Huang J, Jiang JG, Wang DW 2016 An APOC3 3'UTR variant associated with plasma triglycerides levels and coronary heart disease by creating a functional miR-4271 binding site. Sci. Rep. 6, 32700 (doi:10.1038/srep32700)

    Not in our library10.1038/srep32700

  51. 51.

    Caussy C et al. 2014 An APOA5 3' UTR variant associated with plasma triglycerides triggers APOA5 downregulation by creating a functional miR-485-5p binding site. Am. J. Hum. Genet. 94, 129–134. (doi:10.1016/j.ajhg.2013.12.001)

    Not in our library10.1016/j.ajhg.2013.12.001

  52. 52.

    Richardson K et al. 2011 The PLIN4 variant rs8887 modulates obesity related phenotypes in humans through creation of a novel miR-522 seed site. PLoS ONE 6, e17944 (doi:10.1371/journal.pone.0017944)

    Not in our library10.1371/journal.pone.0017944

  53. 53.

    Bandiera S et al. 2013 Genetic variations creating microRNA target sites in the FXN 3'-UTR affect frataxin expression in Friedreich ataxia. PLoS ONE 8, e54791 (doi:10.1371/journal.pone.0054791)

    Not in our library10.1371/journal.pone.0054791

  54. 54.

    Kabaria S, Choi DC, Chaudhuri AD, Mouradian MM, Junn E 2015 Inhibition of miR-34b and miR-34c enhances alpha-synuclein expression in Parkinson's disease. FEBS Lett. 589, 319–325. (doi:10.1016/j.febslet.2014.12.014)

    Not in our library10.1016/j.febslet.2014.12.014

  55. 55.
  56. 56.

    Wu S et al. 2016 MicroRNA-137 inhibits EFNB2 expression affected by a genetic variant and is expressed aberrantly in peripheral blood of schizophrenia patients. EBioMedicine 12, 133–142. (doi:10.1016/j.ebiom.2016.09.012)

    Not in our library10.1016/j.ebiom.2016.09.012

  57. 57.

    Wang X et al. 2016 Single nucleotide polymorphism in the microRNA-199a binding site of HIF1A gene is associated with pancreatic ductal adenocarcinoma risk and worse clinical outcomes. Oncotarget 7, 13 717–13 729. (doi:10.18632/oncotarget.7263)

    Not in our library10.18632/oncotarget.7263

  58. 58.

    Yuan L et al. 2013 Genetic variation in DROSHA 3'UTR regulated by hsa-miR-27b is associated with bladder cancer risk. PLoS ONE 8, e81524 (doi:10.1371/journal.pone.0081524)

    Not in our library10.1371/journal.pone.0081524

  59. 59.

    Wang G, van der Walt JM, Mayhew G, Li Y-J, Züchner S, Scott WK, Martin ER, Vance JM 2008 Variation in the miRNA-433 binding site of FGF20 confers risk for Parkinson disease by overexpression of alpha-synuclein. Am. J. Hum. Genet. 82, 283–289. (doi:10.1016/j.ajhg.2007.09.021)

    Not in our library10.1016/j.ajhg.2007.09.021

  60. 60.

    Abelson JF et al. 2005 Sequence variants in SLITRK1 are associated with Tourette's syndrome. Science 310, 317–320. (doi:10.1126/science.1116502)

    Not in our library10.1126/science.1116502

  61. 61.

    Zuchner S, Wang G, Tran-Viet K-N, Nance MA, Gaskell PC, Vance JM, Ashley-Koch AE, Pericak-Vance MA 2006 Mutations in the novel mitochondrial protein REEP1 cause hereditary spastic paraplegia type 31. Am. J. Hum. Genet. 79, 365–369. (doi:10.1086/505361)

    Not in our library10.1086/505361

  62. 62.

    Beetz C et al. 2008 REEP1 mutation spectrum and genotype/phenotype correlation in hereditary spastic paraplegia type 31. Brain 131, 1078–1086. (doi:10.1093/brain/awn026)

    Not in our library10.1093/brain/awn026

  63. 63.

    Adams BD, Furneaux H, White BA 2007 The micro-ribonucleic acid (miRNA) miR-206 targets the human estrogen receptor-alpha (ERalpha) and represses ERalpha messenger RNA and protein expression in breast cancer cell lines. Mol. Endocrinol. 21, 1132–1147. (doi:10.1210/me.2007-0022)

    Not in our library10.1210/me.2007-0022

  64. 64.

    Sethupathy P, Borel C, Gagnebin M, Grant GR, Deutsch S, Elton TS, Hatzigeorgiou AG, Antonarakis SE 2007 Human microRNA-155 on chromosome 21 differentially interacts with its polymorphic target in the AGTR1 3′ untranslated region: a mechanism for functional single-nucleotide polymorphisms related to phenotypes. Am. J. Hum. Genet. 81, 405–413. (doi:10.1086/519979)

    Not in our library10.1086/519979

  65. 65.

    Mishra PJ, Humeniuk R, Longo-Sorbello GS, Banerjee D, Bertino JR 2007 A miR-24 microRNA binding-site polymorphism in dihydrofolate reductase gene leads to methotrexate resistance. Proc. Natl Acad. Sci. USA 104, 13 513–13 518. (doi:10.1073/pnas.0706217104)

    Not in our library10.1073/pnas.0706217104

  66. 66.

    Tan Z et al. 2007 Allele-specific targeting of microRNAs to HLA-G and risk of asthma. Am. J. Hum. Genet. 81, 829–834. (doi:10.1086/521200)

    Not in our library10.1086/521200

  67. 67.

    Jensen KP, Covault J, Conner TS, Tennen H, Kranzler HR, Furneaux HM 2009 A common polymorphism in serotonin receptor 1B mRNA moderates regulation by miR-96 and associates with aggressive human behaviors. Mol. Psychiatry 14, 381–389. (doi:10.1038/mp.2008.15)

    Not in our library10.1038/mp.2008.15

  68. 68.

    Landi D et al. 2008 Polymorphisms within micro-RNA-binding sites and risk of sporadic colorectal cancer. Carcinogenesis 29, 579–584. (doi:10.1093/carcin/bgm304)

    Not in our library10.1093/carcin/bgm304

  69. 69.

    Kapeller J et al. 2008 First evidence for an association of a functional variant in the microRNA-510 target site of the serotonin receptor-type 3E gene with diarrhea predominant irritable bowel syndrome. Hum. Mol. Genet. 17, 2967–2977. (doi:10.1093/hmg/ddn195)

    Not in our library10.1093/hmg/ddn195

  70. 70.

    Wade M, Wang YV, Wahl GM 2010 The p53 orchestra: Mdm2 and Mdmx set the tone. Trends Cell Biol. 20, 299–309. (doi:10.1016/j.tcb.2010.01.009)

    Not in our library10.1016/j.tcb.2010.01.009

  71. 71.

    Xu C et al. 2016 MDM4 rs4245739 A > C polymorphism correlates with reduced overall cancer risk in a meta-analysis of 69477 subjects. Oncotarget 7, 71 718–71 726. (doi:10.18632/oncotarget.12326)

    Not in our library10.18632/oncotarget.12326

  72. 72.

    Westfall MD, Pietenpol JA 2004 p63: Molecular complexity in development and cancer. Carcinogenesis 25, 857–864. (doi:10.1093/carcin/bgh148)

    Not in our library10.1093/carcin/bgh148

  73. 73.

    Park E, Kim H, Kim JM, Primack B, Vidal-Cardenas S, Xu Y, Price BD, Mills AA, D'Andrea AD 2013 FANCD2 activates transcription of TAp63 and suppresses tumorigenesis. Mol. Cell 50, 908–918. (doi:10.1016/j.molcel.2013.05.017)

    Not in our library10.1016/j.molcel.2013.05.017

  74. 74.

    Guo X, Keyes WM, Papazoglu C, Zuber J, Li W, Lowe SW, Vogel H, Mills AA 2009 TAp63 induces senescence and suppresses tumorigenesis in vivo. Nat. Cell Biol. 11, 1451–1457. (doi:10.1038/ncb1988)

    Not in our library10.1038/ncb1988

  75. 75.

    Flores ER, Tsai KY, Crowley D, Sengupta S, Yang A, McKeon F, Jacks T 2002 p63 and p73 are required for p53-dependent apoptosis in response to DNA damage. Nature 416, 560–564. (doi:10.1038/416560a)

    Not in our library10.1038/416560a

  76. 76.

    Coffinier C, Thepot D, Babinet C, Yaniv M, Barra J 1999 Essential role for the homeoprotein vHNF1/HNF1 beta in visceral endoderm differentiation. Development 126, 4785–4794.

    Not in our library10.1242/dev.126.21.4785

  77. 77.

    Kornfeld JW et al. 2013 Obesity-induced overexpression of miR-802 impairs glucose metabolism through silencing of Hnf1b. Nature 494, 111–115. (doi:10.1038/nature11793)

    Not in our library10.1038/nature11793

  78. 78.
  79. 79.

    Andersson I, Arner P, Mottagui-Tabar S, Rydén M, Löfgren P, Faulds G, Hoffstedt J, Brookes AJ 2003 Evidence for an important role of perilipin in the regulation of human adipocyte lipolysis. Diabetologia 46, 789–797. (doi:10.1007/s00125-003-1112-x)

    Not in our library10.1007/s00125-003-1112-x

  80. 80.

    Wang Y et al. 2003 Perilipin expression in human adipose tissues: effects of severe obesity, gender, and depot. Obes. Res. 11, 930–936. (doi:10.1038/oby.2003.128)

    Not in our library10.1038/oby.2003.128

  81. 81.

    Marmolino D 2011 Friedreich's ataxia: past, present and future. Brain Res. Rev. 67, 311–330. (doi:10.1016/j.brainresrev.2011.04.001)

    Not in our library10.1016/j.brainresrev.2011.04.001

  82. 82.

    Sonntag KC, Woo TU, Krichevsky AM 2012 Converging miRNA functions in diverse brain disorders: a case for miR-124 and miR-126. Exp. Neurol. 235, 427–435. (doi:10.1016/j.expneurol.2011.11.035)

    Not in our library10.1016/j.expneurol.2011.11.035

  83. 83.

    Mahishi LH, Hart RP, Lynch DR, Ratan RR 2012 miR-886-3p levels are elevated in Friedreich ataxia. J. Neurosci. 32, 9369–9373. (doi:10.1523/JNEUROSCI.0059-12.2012)

    Not in our library10.1523/jneurosci.0059-12.2012

  84. 84.

    Sotiriou S, Gibney G, Baxevanis AD, Nussbaum RL 2009 A single nucleotide polymorphism in the 3'UTR of the SNCA gene encoding alpha-synuclein is a new potential susceptibility locus for Parkinson disease. Neurosci. Lett. 461, 196–201. (doi:10.1016/j.neulet.2009.06.034)

    Not in our library10.1016/j.neulet.2009.06.034

  85. 85.

    Del Tredici K, Braak H 2016 Review: Sporadic Parkinson's disease: development and distribution of alpha-synuclein pathology. Neuropathol. Appl. Neurobiol. 42, 33–50. (doi:10.1111/nan.12298)

    Not in our library10.1111/nan.12298

  86. 86.

    Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R 2004 Fast and effective prediction of microRNA/target duplexes. RNA 10, 1507–1517. (doi:10.1261/rna.5248604)

    Not in our library10.1261/rna.5248604

  87. 87.

    Goicoechea SM, Bednarski B, García-Mata R, Prentice-Dunn H, Kim HJ, Otey CA 2009 Palladin contributes to invasive motility in human breast cancer cells. Oncogene 28, 587–598. (doi:10.1038/onc.2008.408)

    Not in our library10.1038/onc.2008.408

  88. 88.

    Gucciardo E, Lohi J, Li R, Sugiyama N, Carpen O, Lehti K 2014 Actin-associated protein palladin promotes tumor cell invasion by linking extracellular matrix degradation to cell cytoskeleton. Mol. Biol. Cell 25, 2556–2570. (doi:10.1091/mbc.E13-11-0667)

    Not in our library10.1091/mbc.e13-11-0667

  89. 89.

    Senturk A, Pfennig S, Weiss A, Burk K, Acker-Palmer A 2011 Ephrin Bs are essential components of the Reelin pathway to regulate neuronal migration. Nature 472, 356–360. (doi:10.1038/nature09874)

    Not in our library10.1038/nature09874

  90. 90.

    Janaszak-Jasiecka A, Bartoszewska S, Kochan K, Piotrowski A, Kalinowski L, Kamysz W, Ochocka RJ 2016 miR-429 regulates the transition between hypoxia-inducible factor (HIF)1A and HIF3A expression in human endothelial cells. Sci. Rep. 6, 22775 (doi:10.1038/srep22775)

    Not in our library10.1038/srep22775

  91. 91.

    Bartoszewska S, Kochan K, Piotrowski A, Kamysz W, Ochocka RJ, Collawn JF, Bartoszewski R 2015 The hypoxia-inducible miR-429 regulates hypoxia-inducible factor-1α expression in human endothelial cells through a negative feedback loop. FASEB J 29, 1467–1479. (doi:10.1096/fj.14-267054)

    Not in our library10.1096/fj.14-267054

  92. 92.

    Madanecki P, Kapoor N, Bebok Z, Ochocka R, Collawn J, Bartoszewski R 2013 Regulation of angiogenesis by hypoxia: the role of microRNA. Cell Mol. Biol. Lett. 18, 47–57. (doi:10.2478/s11658-012-0037-0)

    Not in our library10.2478/s11658-012-0037-0

  93. 93.

    Rane S, He M, Sayed D, Vashistha H, Malhotra A, Sadoshima J, Vatner DE, Vatner SF, Abdellatif M 2009 Downregulation of MiR-199a derepresses hypoxia-inducible factor-1α and Sirtuin 1 and recapitulates hypoxia preconditioning in cardiac myocytes. Circ. Res. 104, 879–886. (doi:10.1161/CIRCRESAHA.108.193102)

    Not in our library10.1161/circresaha.108.193102

  94. 94.

    Guo X, Li D, Chen Y, An J, Wang K, Xu Z, Chen Z, Xing J 2015 SNP rs2057482 in HIF1A gene predicts clinical outcome of aggressive hepatocellular carcinoma patients after surgery. Sci. Rep. 5, 11846 (doi:10.1038/srep11846)

    Not in our library10.1038/srep11846

  95. 95.

    Gurtner A, Falcone E, Garibaldi F, Piaggio G 2016 Dysregulation of microRNA biogenesis in cancer: the impact of mutant p53 on Drosha complex activity. J. Exp. Clin. Cancer Res. 35, 45 (doi:10.1186/s13046-016-0319-x)

    Not in our library10.1186/s13046-016-0319-x

  96. 96.

    Zhang Z, Zhang G, Kong C, Bi J, Gong D, Yu X, Shi D, Zhan B, Ye P 2015 EIF2C, Dicer, and Drosha are up-regulated along tumor progression and associated with poor prognosis in bladder carcinoma. Tumour Biol. 36, 5071–5079. (doi:10.1007/s13277-015-3158-z)

    Not in our library10.1007/s13277-015-3158-z

  97. 97.
  98. 98.
  99. 99.
  100. 100.

    Ryan BC, Werner TS, Howard PL, Chow RL 2016 ImiRP: a computational approach to microRNA target site mutation. BMC Bioinform. 17, 190 (doi:10.1186/s12859-016-1057-y)

    Not in our library10.1186/s12859-016-1057-y

  101. 101.

    Melo SA, Esteller M 2011 Dysregulation of microRNAs in cancer: playing with fire. FEBS Lett. 585, 2087–2099. (doi:10.1016/j.febslet.2010.08.009)

    Not in our library10.1016/j.febslet.2010.08.009

  102. 102.

    Shin K-J et al. 2006 A single lentiviral vector platform for microRNA-based conditional RNA interference and coordinated transgene expression. Proc. Natl Acad. Sci. USA 103, 13 759–13 764. (doi:10.1073/pnas.0606179103)

    Not in our library10.1073/pnas.0606179103

  103. 103.

    Amantana A, Iversen PL 2005 Pharmacokinetics and biodistribution of phosphorodiamidate morpholino antisense oligomers. Curr. Opin. Pharmacol. 5, 550–555. (doi:10.1016/j.coph.2005.07.001)

    Not in our library10.1016/j.coph.2005.07.001

  104. 104.

    Summerton J 1999 Morpholino antisense oligomers: the case for an RNase H-independent structural type. Biochim. Biophys. Acta 1489, 141–158. (doi:10.1016/S0167-4781(99)00150-5)

    Not in our library10.1016/s0167-4781(99)00150-5

  105. 105.

    Staton AA, Giraldez AJ 2011 Use of target protector morpholinos to analyze the physiological roles of specific miRNA-mRNA pairs in vivo. Nat. Protoc. 6, 2035–2049. (doi:10.1038/nprot.2011.423)

    Not in our library10.1038/nprot.2011.423

  106. 106.

    Vogel C, Marcotte EM 2012 Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet. 13, 227–232. (doi:10.1038/nrg3185)

    Not in our library10.1038/nrg3185

  107. 107.

    Porteus MH 2015 Towards a new era in medicine: therapeutic genome editing. Genome Biol. 16, 286 (doi:10.1186/s13059-015-0859-y)

    Not in our library10.1186/s13059-015-0859-y

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