Computational identification and experimental validation of microRNAs binding to the Alzheimer-related gene ADAM10
© Augustin et al.; licensee BioMed Central Ltd. 2012
Received: 22 December 2011
Accepted: 19 April 2012
Published: 17 May 2012
MicroRNAs (miRNAs) are post-transcriptional regulators involved in numerous biological processes including the pathogenesis of Alzheimer’s disease (AD). A key gene of AD, ADAM10, controls the proteolytic processing of APP and the formation of the amyloid plaques and is known to be regulated by miRNA in hepatic cancer cell lines. To predict miRNAs regulating ADAM10 expression concerning AD, we developed a computational approach.
MiRNA binding sites in the human ADAM10 3' untranslated region were predicted using the RNA22, RNAhybrid and miRanda programs and ranked by specific selection criteria with respect to AD such as differential regulation in AD patients and tissue-specific expression. Furthermore, target genes of miR-103, miR-107 and miR-1306 were derived from six publicly available miRNA target site prediction databases. Only target genes predicted in at least four out of six databases in the case of miR-103 and miR-107 were compared to genes listed in the AlzGene database including genes possibly involved in AD. In addition, the target genes were used for Gene Ontology analysis and literature mining. Finally, we used a luciferase assay to verify the potential effect of these three miRNAs on ADAM10 3'UTR in SH-SY5Y cells.
Eleven miRNAs were selected, which have evolutionary conserved binding sites. Three of them (miR-103, miR-107, miR-1306) were further analysed as they are linked to AD and most strictly conserved between different species. Predicted target genes of miR-103 (p-value = 0.0065) and miR-107 (p-value = 0.0009) showed significant overlap with the AlzGene database except for miR-1306. Interactions between miR-103 and miR-107 to genes were revealed playing a role in processes leading to AD. ADAM10 expression in the reporter assay was reduced by miR-1306 (28%), miR-103 (45%) and miR-107 (52%).
Our approach shows the requirement of incorporating specific, disease-associated selection criteria into the prediction process to reduce the amount of false positive predictions. In summary, our method identified three miRNAs strongly suggested to be involved in AD, which possibly regulate ADAM10 expression and hence offer possibilities for the development of therapeutic treatments of AD.
MicroRNAs (miRNAs) are on average 22 nucleotides long and play a pivotal role in gene regulation. These small RNAs regulate the gene expression post-transcriptionally by suppression of mRNA translation, stimulation of mRNA deadenylation and degradation or induction of target mRNA cleavage, but have also the potential to activate translation [1, 2]. Over half of the mammalian protein coding-genes are regulated by miRNAs and most human mRNAs have binding sites for miRNAs . The interaction of miRNA and target mRNA requires base pairing between the seed sequence (positions 2–8) of the miRNA at the 5′ end and a sequence most frequently found in the 3′ untranslated region (UTR) of the target mRNA . MiRNAs are involved in neuronal functions like neurite outgrowth and brain development. They were recently described to play a role in human neurodegenerative diseases. Changes in miRNA expression profiles or miRNA target sequences could contribute to the development of Parkinson’s disease and Alzheimer’s disease (AD) [5, 6].
Characteristics of AD are insoluble plaques of amyloid β (Aβ) peptides emerging from the cleavage of the amyloid beta precursor protein (APP) and neuro-fibrillary tangles in the brains of AD patients [7, 8]. The alpha-secretase “a disintegrin and metalloproteinase 10” (ADAM10) [9–11] generates soluble secreted amyloid precursor protein-alpha (sAPPα) and avoids formation of plaques, because it cleaves APP inside the Aβ sequence .
Numerous available computational methods predict a large number of genes targeted by miRNAs regulating gene expression, but only few have been validated experimentally. Many computational predictions are false positives and therefore have to be filtered out . The requirement of target-site conservation in different species including far related species would be a potential way to reduce the false positive rate .
In this study we established an approach to identify miRNAs regulating ADAM10 expression which therefore might influence the progression of AD. The three programs RNA22, RNAhybrid and miRanda predicted potential miRNA binding sites to ADAM10. We sought to identify the most interesting miRNAs possibly binding to ADAM10 with additional selection criteria in particular whether they play a role in AD. Additionally, the most interesting miRNAs were experimentally verified by a luciferase assay. Our results show that miR-103, miR-107 and miR-1306 influence the expression of ADAM10 at least in the reporter assay system. These miRNAs could play a role in AD and therefore are interesting candidates to be further analysed concerning their biological function and relation to AD.
miRNA target site prediction databases
MiRNA binding sites to target genes were downloaded from seven different databases: miRBase, 5-Nov-2007, http://www.mirbase.org/; microRNA, September 2008 Release, http://www.microrna.org/microrna/home.do; PicTar via UCSC Table Browser, assembly = May 2004 (NCBI35/hg17), group = Regulation, track = PicTar miRNA, http://genome.ucsc.edu/; PITA, version 6 (31-Aug-2008), http://genie.weizmann.ac.il/pubs/mir07/index.html; RNA22, March 2007, http://cbcsrv.watson.ibm.com/rna22.html; TarBase, June 2008, http://diana.cslab.ece.ntua.gr/tarbase/; TargetScan, Release 5, http://www.targetscan.org/. We established a workflow considering all miRNA target site predictions downloaded.
miRNA target prediction
We used three prediction programs RNA22, RNAhybrid, miRanda and predicted all binding sites of the miRNA sequences to the 3'UTR sequence of human ADAM10.
RNA22 is a pattern-based method for the identification of miRNA-target sites. The method has high sensitivity, is resilient to noise, can be applied to the analysis of any genome without requiring genome-specific retraining and does not rely upon cross-species conservation. Focusing on novel features of miRNA-mRNA interaction RNA22 first finds putative miRNA binding sites in the sequence of interest then identifies the targeting miRNA and hence allows to identify sites targeted by yet-undiscovered miRNAs. An implementation of RNA22 (19-May-2008) is available online at http://cbcsrv.watson.ibm.com/rna22.html[19, 21].
The second program RNAhybrid is an extension of the classical RNA secondary structure prediction algorithm from Zuker and Stiegler . It finds the energetically most favorable hybridization sites of a small RNA in a large RNA incorporating ‘seed-match speed-up’, which first searches for seed matches in the candidate targets and only upon finding such matches the complete hybridization around the seed-match is calculated. The user can define the position and length of the seed region with the option to allow for G:U wobble base pairs in the seed pairing. Intramolecular base pairings and branching structures are forbidden and statistical significance of predicted targets is assessed with an extreme value statistics of length normalized minimum free energies, a Poisson approximation of multiple binding sites, and the calculation of effective numbers of orthologous targets in comparative studies of multiple organisms. RNAhybrid, Version 2.1, is available online at http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/[23, 24].
The miRanda algorithm is similar to the Smith-Waterman algorithm, but scores based on the complementarity of nucleotides (A = U or G ≡ C) and one G:U wobble pair is allowed in the seed region but has to be compensated by matches in the 3′ end of miRNA. In order to estimate the thermodynamic properties of a predicted pairing between miRNA and 3′UTR sequence, the algorithm uses folding routines from the Vienna 1.3 RNA secondary structure programming library (RNAlib) . A conservation filter is used and optionally some rudimentary statistics about each target site can be generated. MiRanda, September 2008 Release, is available online at http://www.microrna.org/microrna/home.do[21, 26].
The parameter setting for RNA22 is: maximum number of “UN-paired” bases within the extent of the seed = 0, extent of seed in nucleotides = 6, minimum number of paired-up bases that you want to see in any reported heteroduplex = 14, maximum value for the folding energy in any reported heteroduplex = −25 kcal/mol. The parameter setting for RNAhybrid is: “-s 3utr_human” (“-s” tells RNAhybrid to quickly estimate statistical parameters from “minimal duplex energies” under the assumption that the target sequences are human 3′UTR sequences). The parameter setting for miRanda is the default parameter setting: gap open penalty = −8, gap extend = −2, score threshold = 50, energy threshold = −20 kcal/mol, scaling parameter = 4.
We retrieved the 3′UTR sequence of ADAM10 (human ADAM10 3′UTR based on transcript NM_001110 (chr15:58888510–58889745)) from NCBI http://www.ncbi.nlm.nih.gov/. We downloaded 703 mature miRNA sequences for Homo sapiens from miRBase, version 13.0 http://www.mirbase.org/.
Extraction of best miRNA predictions
The extraction of miRNAs was applied according to the following selection criteria. We checked for each miRNA how many programs predicted the miRNA to bind to human ADAM10 3′UTR. The regulation of miRNAs in AD was verified by the publication of Cogswell et al. , which provides a list of miRNAs expressed in the tissues hippocampus, cerebellum and medial frontal gyrus. Another possibility to check the expression of miRNAs in the brain is the Mouse Genome Informatics (MGI) database (Mouse Genome Database, The Jackson Laboratory, Bar Harbor, Maine; http://www.informatics.jax.org/) . Literature search by PubMed was done as an additional approval, to search for already described target genes of the miRNAs, especially for target genes involved in AD. Mouse ADAM10 3′UTR based on transcript NM_007399 (chr9:70625902–70628036) from NCBI http://www.ncbi.nlm.nih.gov/ was used for binding site search of mouse miRNAs from miRBase, version 13.0 http://www.mirbase.org/. The parameter setting for RNA22 and miRanda is the same as for human miRNA binding site prediction at the human ADAM10 3′UTR. The parameter setting for RNAhybrid is “-d 1.9,0.28” (1.9 is the location parameter and 0.28 the shape parameter of the assumed extreme value distribution). Additionally, we searched by TargetScan database http://www.targetscan.org/ and microRNA database http://www.microrna.org/microrna/home.do for miRNAs binding to human ADAM10 3′UTR and compared the TargetScan and microRNA predictions to our list of miRNAs for equal miRNAs. We identified the number of binding sites of a miRNA in the human ADAM10 3′UTR predicted by each program. ADAM10 3′UTR sequences from ten different species were analysed for conserved regions. The following sequences where taken: human ADAM10 3′UTR from transcript NM_001110 (chr15:58888510–58889745), mouse ADAM10 3′UTR from transcript NM_007399 (chr9:70625902–70628036), horse ADAM10 3′UTR from transcript XM_001498169.1 (chr1:132875124–132876868), dog ADAM10 3′UTR from transcript XM_858910 (chr30:26596273–26598436), chimp ADAM10 3′UTR from transcript XM_001172393.1 (chr15:55942343–55944774), chicken ADAM10 3′UTR from transcript ENSGALT00000034458 (chr10:7949768–7951846), rhesus monkey ADAM10 3′UTR from transcript XM_001096908 (chr7:36929437–36932008), zebra fish ADAM10 3′UTR from transcript NM_001159314 (chr7:31745579–31747655), opossum ADAM10 3′UTR from transcript ENSMODT00000011088 (chr1:162230000–162230183), zebra finch ADAM10 3′UTR from transcript XR_054746 (chr10:6638729–6639273). For multiple sequence alignment of the ten ADAM10 3′UTR sequences we applied ClustalW Version 2.1 from the European Bioinformatics Institute (EBI) http://www.ebi.ac.uk/[29, 30]. We used default parameters except: DNA Weight Matrix = ‘ClustalW’, Clustering = ‘UPGMA’. After extraction of the conserved regions between at least seven species we looked for miRNA binding sites localized in these conserved regions. Additionally, we determined the conservation (given in percentage) of the miRNA binding site sequence from human to each species.
Statistical analysis was performed with R statistical software (R 2.8.0, http://www.r-project.org/). The p-value was computed by the R function fisher.test with default settings. The Fisher’s exact test is used to examine the significance of the association (contingency) between the two kinds of classification. Significantly regulated genes were considered, if the p-value is equal or below 0.05. We generated Venn diagrams to see the overlap between target genes of miR-103 and miR-107 common in 4 out of 6 databases as well as genes in the AlzGene database (http://www.alzgene.org/; Version: 20.06.2011) . Each set of target genes of miR-103 and miR-107 common in 4 out of 6 databases as well as the set of target genes of miR-1306 in the database PITA was explored for enrichment in Gene Ontology  by the software Pathway Studio 8.0 (Ariadne Genomics) based on database ResNet 8.0.
Literature mining and pathway analysis
Literature search by PubMed was done to extract information about the target genes of the miRNAs resulting from Pathway Studio analysis and their relation to AD. To verify the miRNAs searches were performed for miRNA interactions in all PubMed abstracts with the help of the text mining program Pathway Studio 8.0 (Ariadne Genomics) based on the Natural Language Processing (NLP) Technology. Pathway analysis was done with the software Ingenuity Systems IPA 9.0 (http://www.ingenuity.com/) especially with the Path Designer.
Mature miRNAs and the inactive negative control were from Invitrogen (No. PM11012, PM13206, PM10632, PM10056). All RNA species were dissolved to 5 pmol/μl in nuclease-free water upon arrival, aliquoted and stored at −20°C.
Cloning of the ADAM10 3′UTR luciferase reporter construct
The 3′UTR of human ADAM10 was amplified from THP-1 chromosomal DNA using the FailSafe PCR kit (Epicentre) and the following primers:
AD10_3UTR_for 5′GCGGCCGCGCCCATTCAGCAACCCCAG 3′
AD10_3UTR_rev 5′GCGGCCGCCACTTGTGCCCGTAGCAGCC 3′.
The obtained DNA fragment was verified by restriction digestion and sequencing. The 3′UTR was subsequently cloned into the NotI site of the pCMV-GLuc vector (NEB), which allows to monitor regulated Gaussia luciferase expression in the cell supernatant.
SH-SY5Y cells were cultivated in phenol red-free DMEM/F12, supplemented with 10% FCS and 1% glutamine at 37°C, 95% air moisture, 5% CO2 and passaged twice a week with a splitting rate of ½ to ¼.
3′UTR luciferase reporter assay
Retro-transfection was performed using 0.005 μl Lipofectamine 2000 (Invitrogen) per μl OptiMEM-medium and 0.1 pmol/μl miRNA (Invitrogen) or negative control. For combination of miRNA 1306 together with miRNA 103 or 107 a concentration of 0.05 pmol/μl each was used. 2 ng/μl endotoxin-free plasmid DNA of the 3′UTR-reporter vector were added to 45.000 cells per well in 96 well format. Control cells were mock-treated with nuclease-free water instead of RNA molecules.
5 hrs after transfection, the cell supernatant was exchanged to 200 μl culture medium per well. In a preliminary experiment, 10 μl cell supernatant were collected at various time points over a 72 hour period; 48 hours were determined to be the optimal incubation time (data not shown). Therefore, 10 μl cell supernatant were aspirated 48 hours after transfection and stored at −20°C until samples were measured. Secreted Gaussia luciferase was quantitatively analyzed (Renilla-Luciferase assay, Promega) using the FluostarOptima luminometer (BMG). Cell densities were checked by quantitation of protein content in the cell lysate by NanoQuant assay (Roth).
Results and discussion
122 miRNAs are predicted by at least two programs to bind to human ADAM10 3′UTR sequence and 52 of them are significant according to expression and selection criteria described in the following. To consider different aspects of the distinct prediction algorithms at least two programs should predict a miRNA binding site. Important is also the expression of the miRNA in brain provided by the MGI database or the regulation of the miRNA in AD as described by Cogswell and colleagues (2008) in the tissues hippocampus, cerebellum and medial frontal gyrus . An additional confirmation of miRNA being involved in AD is a binding site to a target gene, which is involved in AD, described in the literature and thus the miRNA might regulate also other AD key genes such as ADAM10. Furthermore, the miRNA prediction is strengthened by the corresponding mouse miRNA binding to the mouse ADAM10 3′UTR sequence predicted by at least two of the three miRNA prediction programs RNA22, RNAhybrid and miRanda. Prediction of the miRNA by other webtools such as TargetScan and microRNA is also a confirmation of the miRNA. Multiple binding sites of a single miRNA in the 3′UTR verify the prediction .
List of predicted miRNAs binding to a conserved region of human ADAM10 3'UTR
Differential regulated in AD
Conservation zebra fish
predicted by 3 programs, mouse ADAM10
targets BACE1, predicted by TargetScan, literature for AD, mouse ADAM10
predicted by TargetScan, literature for AD, mouse ADAM10
predicted by microRNA, mouse ADAM10
predicted by microRNA
hippocampus, medial frontal gyrus
predicted by microRNA
predicted by microRNA, mouse ADAM10
predicted by microRNA, mouse ADAM10
Prediction of miRNA target genes and their relation to AD
Additionally, we did a Gene Ontology analysis with the predicted target genes of the three miRNAs to validate the functionality of the miRNAs and their involvement to AD. With the help of the literature mining tool Pathway Studio we searched for molecular functions and biological processes common to the target genes of the three miRNAs. The molecular function calcium ion binding is significant in all three miRNA analyses (miR-103: p-value = 0.0011; miR-107: p-value = 0.0025; miR-1306: p-value = 2.1 × 10−5) and is considered to be involved in AD. Calcium ions are found in an elevated level in tangle-bearing neurons of AD patients compared to healthier neurons . Further, an abnormal increase of intracellular Calcium ion levels in neurites associated with Aβ deposits was demonstrated in a mouse model of AD . An additional evidence is given by the significant biological processes learning (miR-103: p-value = 0.0008; miR-107: p-value = 3.3 × 10−5; miR-1306: p-value = 0.0003), brain development (miR-103: p-value = 0.0023; miR-107: p-value = 0.0004; miR-1306: p-value = 7.1 × 10−6) and nervous system development (miR-103: p-value = 0.0004; miR-107: p-value = 0.0004; miR-1306: p-value = 6.9 × 10−8) that the three miRNAs are involved in AD. Mouse models with an overexpression of ADAM10 showed a positive effect of the α-secretase on learning and memory and mice with a dominant negative mutant form of ADAM10 had learning deficiencies . Environmental influences occurring during brain development predefine the expression and regulation of APP. As a consequence levels of APP and Aβ are increased causing AD later in life . The Aβ fragments forming plaques are of varying length depending on the site of cleavage. The Aβ42 fragment is a ligand for the cellular prion protein, which is important for nervous system development . (See Additional files 2,3 and 4: List of enriched target genes of miR-103/107/1306 in Gene Ontology).
Experimental validation of bioinformatically predicted miRNAs
We established a computational approach for the identification of miRNAs putatively influencing the expression of ADAM10. A potential functionality of selected miRNAs 103, 107 and 1306 was confirmed by 3′UTR luciferase reporter assay. These results show that the evolutionary conservation of the target gene binding site facilitates miRNA candidate selection independently from the disease for further experimental validation. Moreover, these experiments underline the reliability of our computational approach, which is a combination of characteristics of the prediction software and specific selection criteria for filtering out false positive predictions: disease relevance, specificity of expression, evolutionary conservation of binding sites and occurrence of multiple binding sites. This workflow can also be applied to key genes of other diseases with adjustment of the selection criteria according to the scientific research interest. Our approach provides a new selection tool for identification and ranking of AD-related miRNAs, but to elucidate a profound pathological role of selected candidates, further experiments have to be done.
Canis lupus familiaris
- Rhesus monkey:
- Zebra fish:
- Zebra finch:
We thank Bernd Lentes for technical assistance. This work was supported by the Federal Ministry of Education and Research (BMBF) in the framework of the National Genome Research Network (NGFN), Förderkennzeichen FKZ01GS08130, by the BMBF Project “Kompetenznetzwerk Demenzen - Neurodegeneration” (KNDD, FKZ 01 G1 704) to SFL and WW, by the BMBF Project “Helmholtz Alliance for Mental Health in an Ageing Society” (HelMA, HA215), by the Deutsche Forschungsgemeinschaft (DFG) Project SFB 596 TP A 12 and by the NGFN Project (FKZ 01GS08133).
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