A gene expression restriction network mediated by sense and antisense Alu sequences located on protein-coding messenger RNAs
© Liang and Yeh; licensee BioMed Central Ltd. 2013
Received: 4 September 2012
Accepted: 7 May 2013
Published: 11 May 2013
Alus are primate-specific retrotransposons which account for 10.6% of the human genome. A large number of protein-coding mRNAs are encoded with sense or antisense Alus in the un-translated regions.
We postulated that mRNAs carrying Alus in the two opposite directions can generate double stranded RNAs, capable of regulating the levels of other Alu-carrying mRNAs post-transcriptionally. A gene expression profiling assay showed that the levels of antisense and sense Alus-carrying mRNAs were suppressed in a reversible manner by over-expression of exogenous sense and antisense Alus derived from mRNAs (Family-wise error rate P= 0.0483 and P < 0.0001 respectively). Screening through human mRNAs on the NCBI-RefSeq database, it was found that sense and antisense Alu-carrying transcripts were enriched in distinct cellular functions. Antisense Alu-carrying genes were particularly enriched in neurological and developmental processes, while sense Alu-carrying genes were enriched in immunological functions.
Taken together, we proposed a novel Alu-mediated regulation network capable of stabilizing Alu-carrying mRNA levels in different cell types and restricting the activated expression levels of protein-coding, Alu-carrying mRNAs.
KeywordsAntisense Alu Gene expression restriction Double-stranded Alu Alu-carrying protein-coding RNA
An intriguing characteristic of the human genome is its containing of vast numbers of Alus, a class of short-interspersed repetitive sequences with a length of 280~300 nucleotide bases [1–3]. More than one million copies of Alus altogether contribute 10.6% of the human genome [1, 2]. Alus were retrotransposons evolved from a duplication of the 7SL RNA gene more than 65 million years ago [1–4]. The retrotransposition process of Alus relies on the machinery carried by the long interspersed nucleotide element 1 (L1), another retrotransposon which contributes 17% of the human genome [4, 5]. Alus have diverse sequence variations [6, 7]. A total of 213 Alu subfamilies have been reported based on a thorough computation of sequence homology in the human genome .
Alus were found in both genic and intergenic regions of the human genome , with a higher frequency in the former . Intergenic Alus can be transcribed by polymerase III, yet the transposition activities have remained dormant . Polymerase III-derived Alu transcripts are constantly shattered by Dicer1 in normal human physiology, failure of which may result in Alu toxicity which in turn triggers geographic atrophy , an advanced form of age-related macular degeneration.
Genic Alus have been found in upstream and intronic regions , as well as exonic regions such as 5′ untranslated regions (UTRs)  and 3′UTR of messenger RNAs . Alus in mRNAs are classified as exonic or exonized Alus, depending on whether they are embedded within a longer exon or are spliced into mRNAs as an individual exon. Exonized Alus have been shown to express only occasionally and have low copies of transcripts within cells [12, 13]. Alus have been shown to encompass a 6-base sequence tag complimentary to one common seed of 30 human miRNAs . Recently, long non-coding RNAs have been shown to be capable of binding to Alu-carrying mRNAs, thereby triggering the STAU1-mediated mRNA decay . An analysis of human chromosomes 21 and 22 showed that genic Alus are particularly enriched in genes of metabolism, transport and signaling processes . Despite these analyses, the cellular roles of genic Alus remain largely elusive [3, 8, 11]. Alus were once thought of as parasite-like, selfishly-replicated junk DNAs without prominent constructive roles to human cells [13, 17].
Human mRNAs may carry Alus in either sense or antisense directions. In light of the thermodynamics properties of nucleotide base pairing, we were intrigued to ask whether these mRNAs form double stranded duplex longer than 290 bases, and if that happens, what their corresponding cellular roles could be? Despite the binding of two protein-coding Alu-carrying mRNAs had never been discussed previously to our knowledge, we conjectured that the resulting double stranded RNAs could trigger the post-transcriptional regulation of a large collection of protein-coding mRNAs carrying sense or antisense Alu elements, by offering potent sources of either Dicer1-created short interfering RNA (siRNA) [18–20], or STAU1-mediated mRNA decay . Both mechanisms were originally proposed to address the binding of a non-coding and a protein-coding RNA.
An Alu-carrying mRNA may form a binding with multiple antisense Alu-carrying mRNA, and vice versa. Consequently, mRNAs with sense and antisense Alu elements produce a many-to-many network, where those with the sense elements are prevailingly regulated by those with the antisense elements, resulting in coordinated reaction. Such coordination has been postulated recently on the topic of micro RNAs (miRNA) against genes, pseudogenes and long non-coding RNAs which share the same miRNA targets [21, 22]. Intriguingly, Vidal and colleagues showed that mouse and rat mRNAs carrying sense B1 repeats are expressed coordinately, reaching a maximum level in the G2 phase of the cell cycle . Data showed that the B1 repeats are necessary rather than sufficient criteria for the coordination. It is worth noting that B1 repeats (~140 bases) were also originated from 7SL RNA gene .
Strong sense-antisense bindings of Alu-carrying mRNAs predicted by RNA co-folding computation
Two genes with Alu sense or antisense elements in the 3′UTR
Exon containing the ALU
Protein-coding mRNAs with Alu elements in opposite directions also carry distinct biological functions
The second conjecture was that the duplex of Alu-carrying mRNAs may trigger subsequent degradations of other Alu-carrying RNAs. If such mechanism exists, it follows that sense Alu-carrying mRNAs (referred to as Sens-alus) and antisense Alu-carrying mRNAs (Ant-alus) cannot concurrently stay in high concentrations in human cells. Instead, there are three possibilities: (i) Ant-alu high and Sens-alu low; (ii) Ant-alu low and Sens-alu high; (iii) both Sens-alu and Ant-alu are low. In other words, states (i) and (ii) represent the dominant expression patterns of only one Alu-carrying RNA species. As such, a RNA species might be enriched in certain pathways, while depleted in other pathways, resulting in different functional annotations of the two species. The corresponding null hypothesis is that states (i) and (ii) does not exist and their constituent genes are randomly scattered in a wide spectrum of biological categories and pathways. This hypothesis can be assessed by checking the over and under representation of genes in pathways and biological processes.
We screened Sens-alus and Ant-alus from the entire NCBI-RefSeq human mRNAs  using sequence homology search. A majority of these Alu elements reside in the 3′UTR (99%) and only 1% of them reside in the 5′UTR region. None of them were found to reside completely in the coding region. 689 Ant-alus and 771 Sens-alus were identified respectively, resulting in a total sum of 1460 genes which corresponds to 7.3% of human protein-coding genes (Additional file 1: Table S1, Additional file 1: Table S2). Computational analysis on a random selection of pairs of Ant-alus and Sens-alus showed that all of them can form computational predicted bindings with ΔG lower than -200 kcal/mol. In addition, 190 genes were found to have Alu elements in both sense and antisense directions (Additional file 1: Table S3).
List of all pathways where Sens-alus and Ant-alus are enriched or depleted
Opioid prodynorphin pathway
Muscarinic acetylcholine receptor 2 and 4 signaling pathway
Pyridoxal phosphate salvage pathway
Vitamin B6 metabolism
Metabotropic glutamate receptor group II pathway
5HT1 type receptor mediated signaling pathway
Opioid proopiomelanocortin pathway
Opioid proenkephalin pathway
Metabotropic glutamate receptor group III pathway
Muscarinic acetylcholine receptor 1 and 3 signaling pathway
Beta3 adrenergic receptor signaling pathway
Cortocotropin releasing factor receptor signaling pathway
5HT4 type receptor mediated signaling pathway
p53 pathway feedback loops 2
Angiotensin II-stimulated signaling through G proteins and beta-arrestin
Toll receptor signaling pathway
Nicotinic acetylcholine receptor signaling pathway
Gamma-aminobutyric acid synthesis
Interleukin signaling pathway
Endothelin signaling pathway
List of all biological processes where Ant-alus and Sens-alus are either enriched or depleted
synaptic vesicle exocytosis
polysaccharide metabolic process
establishment or maintenance of chromatin architecture
mammary gland development
glycogen metabolic process
dorsal/ventral axis specification
vitamin catabolic process
cellular amino acid catabolic process
nerve-nerve synaptic transmission
neuromuscular synaptic transmission
response to toxin
response to stimulus
cyclic nucleotide metabolic process
termination of RNA polymerase II transcription
response to interferon-gamma
cytokine-mediated signaling pathway
mRNA 3′-end processing
cellular glucose homeostasis
defense response to bacterium
On the other hand, Sens-alus were over-represented in immunological pathways such as Toll-like receptors, Interleukin and endothelin signaling (P<0.05, Table 2). Sens-alus were not under-represented in any pathways. Sens-alus were also over-represented in biological processes related to cytokine-mediated signaling pathway, responses to interferon gamma and meiosis. Sens-alus were under-represented in synaptic transmission and ectoderm development (Table 3). Interestingly, Ant-alus were over-represented (P = 0.0007) while Sens-alus were under-represented (P = 0.0333) in the biological process of synaptic vesicle exocytosis (Table 3).
Comparison of immune-related genes in Ant-alus and Sens-alus
Toll like receptors
NM_000565.2 ; NM_181359.1
Cluster of differentiation
HLA and related receptors
List of embryonic stem cell related genes in Ant-alus and Sens-alus
Protein angel homolog 2
NM_012117.2; NM_001127321.1; NM_001127322.1
Chromobox protein homolog 5
Signal transducer CD24
Cell division control protein 6 homolog
cadherin 1, type 1, E-cadherin (epithelial)
Cat eye syndrome critical region protein 1
Developmental pluripotency-associated protein 4
Abhydrolase domain-containing protein FAM108B1
N-acetylglucosamine-1-phosphotransferase subunit beta
MHC class I polypeptide-related sequence B
Homeobox protein NANOG
Probable 7,8-dihydro-8-oxoguanine triphosphatase NUDT15
3-phosphoinositide-dependent protein kinase 1
ribosomal protein S24
Ribonucleoside-diphosphate reductase subunit M2
Splicing factor, arginine/serine-rich 1
Teratocarcinoma-derived growth factor 1
Telomeric repeat-binding factor 1
Sodium/potassium-transporting ATPase subunit alpha-2
DNA replication factor Cdt1
Lin-28 homolog A
DNA replication licensing factor MCM4
Condensin complex subunit 2
Nuclear transcription factor Y subunit beta
NM_006452.3; NM_001079524.1; NM_001079525.1
Peroxisomal sarcosine oxidase
Protein phosphatase 1B
PR domain zinc finger protein 14
Serine/threonine-protein kinase PRKX
One possibility for such a difference of Ant-alus and Sens-alus in pathway distributions is that certain genes underwent duplication events, after Alu retrotransposed into these genes, producing a number of paralogs of Alu-carrying genes associated to similar functions. As the primate-specific Alu incorporation events were fairly recent in evolution (~65 million years), these paralogs should still remain in the same protein subfamilies. To check this possibility, we checked the protein subfamilies among Ant-alus and Sens-alus. It was found that 96.2% of Ant-alus and 96.9% of Sens-alus have unique subfamilies (Additional file 1: Table S1, Additional file 1: Table S2), leaving 3.8% of Ant-alus and 3.1% of Sens-alus associated to the same protein subfamilies with others. This suggests that the gene duplication events accounted for a smaller fraction of pathway distributions than direct Alu retrotransposition. That said, gene duplication and Alu incorporation were both parts of evolution which jointly shaped the human genome and its biological functions. The functional annotation was thus based on the final set of Alu-carrying genes till this point in evolution.
Significant suppression of Alu-tagged mRNAs by Alu perturbations
An extrachromosomal replication system was established to examine the perturbation of Alu-carrying genes in response to elevated Alu RNAs in the opposite direction. The null hypothesis here is that the Alu-carrying RNA duplex cannot trigger subsequent post-transcriptional regulation, manifesting a random fluctuation of expression levels. Transfected sense and antisense Alus were first checked to have expressed successfully, by the detection of chimeric RNA sequences expressed from the artificially constructed template sequence encompassing both vector and Alus.
In addition to the GSEA evaluation of group behaviors, we also performed analysis on individual probe sets. Ant-alus were selected if they manifested significant down regulation in response to sense Alu transfections (P< 0.0005, FDR < 0.0321). The fold change of RNA level was between 31.1% and 92.5%. Their expression levels across all 6 conditions were shown as a heatmap in Figure 2C. It showed that in addition to the suppression by sense Alus, the same set of genes can also be suppressed by antisense Alus. Sens-alus were selected if they manifested significant down regulation in response to antisense Alu transfetions (P< 0.0005, FDR < 0.0518). The fold change of RNA level was between 29.6% and 95.4%. Again, the heatmap showed that the same set of genes can also be suppressed by sense Alu (Figure 2D).
We also conducted a smaller-scale experiment for measuring the protein abundance of several randomly selected Sens-alus and Ant-alus, in response to the transfection of Alus in opposite directions, using western blotting. This time, the protein abundances were measured repeatedly once a week up to the 8th week after the selection antibiotics were removed (Figure 2E and 2F). The exogenous sense and antisense Alu RNAs gradually reduced to <25% at week 8 (compared with the maximum level at week 0), and protein suppression effects were observed during the period while the exogeneous Alu was still present.
A regulatory network mediated by Alu RNA duplex
What could be the major driving forces for the dynamics of the network? Environmental stimuli such as stress may be one answer. It was reported that Pol III derived Alu transcripts, usually dormant in normal cell conditions, were elevated by stress such as viral infection [9, 17, 28]. Pol III Alu may be perturbed together with all the other species upon stress response, although the physiological level of perturbation of the four RNA species remained elusive.
Data from the in-vitro system showed that the Sens-alus and Ant-alus were suppressed significantly by transfected Alu counterparts. The transfected sense Alu could represent over-expressed Sens-alus, or the Pol-III derived Alus, as both of them have similar Alu elements in the sense direction to suppress its counterparts. Likewise, the transfected antisense Alu could represent over-expressed Ant-alus, or long non-coding transcripts with antisense Alu elements.
The strong binding of genes with opposite Alu directions was predicted by the RNA folding algorithm. The empirical evidence of the binding was still lacking. We have been planning an experiment based on the idea of using multiple Alu-carrying genes as baits. A binding column will be used to capture the baits. Those RNA bind to the baits can also be captured and then analyzed. This however remained to be our future work.
The network of the Alu-carrying RNAs may underlie the stability and transitions of human cellular states such as neurological or immunological response, as was suggested by the functional annotations of protein-coding Alu-carrying mRNAs. First, the mutual suppression effect may offer barriers among cell lineages. Random fluctuations of Alu-carrying genes may be restricted by the network. Second, upon the invasion of pathogens, the immune system must respond quickly to turn the immature immune cells into mature states by coordinated activations of genes, many of which are Sens-Alus (Table 4).
Activation restriction for gene expression cascades
Previous work by Vidal and colleagues showed that the B1-repeat elements are necessary rather than sufficient criteria for the co-expression of genes, implying that some, but not all, B1-containing genes are activated concurrently . Developmental processes, neurological and immunological functions have been known to comprise many signal transduction events. We continued to reason that, when a signal transmits to a set of Alu-containing genes, they may be activated by way of elevation of their expression levels. The Alu-mediated suppressing effect offers a built-in inhibitory mechanism toward other Alu-tagged RNA species despite the presence of their individual activation signals from the noisy environment. The net effect is a restricted activation of one or few signal transduction, while other temporarily unwanted signals are filtered away. This situation will persist till the formal signaling effect has subsided. Then, another signaling can come through, resulting in a sequence of gene expressions. A full scale of disordered responses is thus prevented, and the activation of Alu-containing genes can proceed in a coordinated fashion, one state after another (Figure 3B and 3C).
One key question about the mutual regulation of Sens-alus and Ant-alus is the responsible molecular mechanisms. Is it through the Dicer1-created siRNA mechanism, the STAU1-mediated RNA degradation, or both? Our data suggested that Dicer1 may play a bigger role. An interesting observation from our experiments is that Alu-carrying genes can be suppressed by transfected Alus in the same direction, although not to the level of statistical significance. These may be explained by the potent source of siRNA offered by the duplex of transfected sense Alu and Ant-alu, upon the cleavage of Dicer1, which may suppress both Sens-alus and Ant-alus depending on the guide strand directions . Sens-alus were thus suppressed by the RNA-induced silencing complex using siRNAs in the antisense strand as the guide strand. Interestingly, Dicer1 is also down regulated in chemically stressed cells . Recent data also showed the intimate trade-off between Dicer1 and Alu abundance .
In summary, we proposed a complex regulation network mediated by the Alu “tags” in four species of RNAs and offered initial evidence. The Alu-mediated suppression effect may restrict the activation of genes with other “tags”, thereby stabilizing state transitions observed along cellular lineages or in response to outside stimuli. Additionally, different ratios of Sens-alus and Ant-alus may be observed in different types of human cells, with two extreme examples of Ant-alus or Sens-alus as the predominant constituents of expressed genes. The former state may be related to neurological functions, while the later may be related to immunological functions.
An in-vitro extrachromosomal replication system was established to examine the postulated regulation effects on genes carrying the Alu elements. Sense and antisense Alus were cloned from cDNAs, which were reversely transcribed from mRNAs of PCM1 (nt 7636 to nt 8082; [RefSeq:NM_006197.3]) and PER2 (nt 5221 to nt 5781; [RefSeq:NM_022817.2]), respectively. The clones were then inserted into pDR2 vectors (Clontech, Mountain View, CA) downstream of the Rous sarcoma virus long terminal repeat (LTR) promoter. This plasmid vector contains Epstein-Barr virus OriP, a gene for hygromycin B selection, and an ampicillin resistance gene. These two plasmids, and a pDR2 vector carrying no additional DNA (the empty vector), were transfected to human embryonic kidney cells constitutively expressing Epstein-Barr virus nuclear antigen-1 (EBNA-1) protein from Epstein-Barr virus (Hek293EBNA cells; Invitrogen, Carlsbad, CA). The three cell lines were maintained in Dulbecco’s modified Eagle’s medium containing 10% fetal bovine serum and 250 ug of G418 per ml. Hygromycin (0.6 mg/ml) was added to the cell culture medium for the selection of stable transformants. After the transfection, the RNA extracts were submitted for RT-PCR, cloning and sequencing to check whether the expressed RNA encompasses both the vector part and the inserted sequence, an evidence that the transfected sequence has successfully expressed in our system. Real-time PCR was also performed to monitor the expression levels of the exogenous transcripts weekly after removal of hygromycin from the culture medium. Upon removal of hygromycin, the extrachromosomal replicating plasmids were gradually lost, allowing for reversion to the un-transfected status.
The mRNA of Sens-alus and Ant-alus, in response to the transfection, were measured by gene expression microarray. Affymetrix Human PrimeView™ arrays were used (Affymetrix, Santa Clara, CA). An in vitro transcription (IVT) with biotinylated ribonucleotide analog were then performed to generate biotin-labeled amplified RNA (aRNA), using GeneChip 3′IVT Express kit (Affymetrix, Santa Clara, CA). The aRNAs were then purified by magnetic beads and fragmented for the subsequent hybridization according to the manufacturer’s protocol. Fluorescent signal was scanned by GeneChip Scanner 3000 7G (Affymetrix, Santa Clara, CA) to produce digital images and then converted and summarized to intensity readings per probe sets (total n= 49395). The protein expression levels of several Alu-containing genes were assayed by western blotting. The microarray raw and normalized data can be found on the NCBI GEO repository by the accession number GSE39822.
The Alu elements in PCM1 (nt 7691 to nt 8008; [RefSeq:NM_006197.3]) and JAK3 (nt 4299 to nt 4614; [RefSeq:NM_000215.3]) were used as query sequences to search against the entire NCBI-RefSeq database  for antisense hits using the command-line Yass alignment software . Standard parameters were used, and hits must have e-values smaller than 10-20 and length longer than 290 bases. This parameter setting allowed non-perfect matches. The coding regions annotated by NCBI-Refseq were also used to discern whether the antisense hits were located in 5′UTR, 3′UTR or the coding regions.
Protein-coding genes were sieved from the Alu-carrying transcripts using the PANTHER (Protein ANalysis THrough Evolutionary Relationships) Classification System version 7.2 on the official bioinformatic site . PANTHER is a sequence based, phylogenic-tree supported system with protein functions annotated by human experts, ensuring a high quality of annotation. Sens-alus were defined by genes with Alus only in the sense direction while Ant-alus were defined by genes with Alus only in the antisense direction. The subfamilies of Sens-alus and Ant-alus were assigned by PANTHER. Functional annotations of over- or under-representation of Ant-alus and Sens-alus amongst various pathways and biological functions were also performed by PANTHER. Gene symbols of 689 and 771 genes were submitted to the website, and the functional annotations of pathways and biological processes of the two lists of genes were calculated concurrently by the system. A total of 165 pathways and 212 biological processes were checked individually to see the level of over- and under-representation of the two lists of genes. The P values were derived using the binormial distribution tests. False discovery rates (FDR) were also calculated to accompany the P values, addressing issues of multiple comparisons. The downloadable results were in the format similar to Tables 2 and 3.
The expression levels across all 18 microarrays (for 6 conditions, each with three biological replicates) were normalized using the RMAExpress (version 1.0.5), implementing the Robust multiarray analysis (RMA) algorithm [33–35]. Gene expression levels per probe set were compared across groups using unpaired two sample t-test assuming unequal variance. False discovery rates (FDR) were used to assess significance in the scenario of multiple comparisons. All P-values were two-tailed.
Perturbation of gene expression levels were evaluated by the stand-alone GSEA software v2.07 offered by the Broad Institute [26, 27]. The goal was to analyze the global perturbations of set of genes of interest, by sense and antisense Alu transfections, in comparison with the control samples of Hek293 cells transfected by empty vectors. GSEA examines whether particular sets of genes (in our case, Ant-alus and Sens-alus) tend to be the leading perturbed genes amongst all genes. When multiple probe-sets are associated to a gene, the median of all probe-set measurements were used to represent the gene. The perturbation was quantified by the difference of gene level between two treatment conditions (i.e. classes). Family-wise error rate (FWER) P-values were derived from an empirical distribution upon 10000 permutations of the class labels to address multiple comparison issues.
The RNA and protein expressions were visualized as heatmaps using Cluster 3.0 [36, 37] and TreeView version 1.1.6r2 . In the heatmap presentation, the expression levels were subtracted by baseline values which were the average measurements on naïve cells and cells with empty vectors.
Antisense Alu-carrying messenger RNA
Sense Alu-carrying messenger RNA
Long interspersed nucleotide element 1
Gene Set Enrichment Analysis
False discovery rate
Family-wise error rate
Robust multiarray analysis.
The authors would like to express their gratitude to the NCBI-RefSeq team for their excellent work on the creation and maintaining of such a high-quality database. We also want to thank the open source bioinformatics community, particularly the YASS team, the Vienna RNA group, the PANTHER group, the GSEA group, the RMAExpress team, the Cluster team, and the TreeView team. Their excellent bioinformatics tools endow enormous values to the entire scientific community.
- Cordaux R, Batzer MA: The impact of retrotransposons on human genome evolution. Nat Rev Genet. 2009, 10: 691-703. 10.1038/nrg2640.PubMed CentralView ArticlePubMedGoogle Scholar
- Price AL, Eskin E, Pevzner PA: Whole-genome analysis of Alu repeat elements reveals complex evolutionary history. Genome Res. 2004, 14: 2245-2252. 10.1101/gr.2693004.PubMed CentralView ArticlePubMedGoogle Scholar
- Batzer M, Deininger P: Alu repeats and human genomic diversity. Nat Rev Genet. 2002, 3: 370-379. 10.1038/nrg798.View ArticlePubMedGoogle Scholar
- Häsler J, Strub K: Alu elements as regulators of gene expression. Nucleic Acids Res. 2006, 34: 5491-5497. 10.1093/nar/gkl706.PubMed CentralView ArticlePubMedGoogle Scholar
- Burns KH, Boeke JD: Human transposon tectonics. Cell. 2012, 149: 740-752. 10.1016/j.cell.2012.04.019.PubMed CentralView ArticlePubMedGoogle Scholar
- Umylny B, Presting G, Efird JT: Most human Alu and murine B1 repeats are unique. J Cell Biochem. 2007, 102: 110-121. 10.1002/jcb.21278.View ArticlePubMedGoogle Scholar
- Bennett EA, Keller H, Mills RE: Active Alu retrotransposons in the human genome. Genome Res. 2008, 18: 1875-1883. 10.1101/gr.081737.108.PubMed CentralView ArticlePubMedGoogle Scholar
- Lander ES, Linton LM, Birren B: Initial sequencing and analysis of the human genome. Nature. 2001, 409: 860-921. 10.1038/35057062.View ArticlePubMedGoogle Scholar
- Chu WM, Liu WM, Schimd CW: RNA polymerase III promoter and terminator elements affect Alu RNA expression. Nucleic Acids Res. 1995, 23: 1750-1757. 10.1093/nar/23.10.1750.PubMed CentralView ArticlePubMedGoogle Scholar
- Kaneko H, Dridi S, Tarallo V: DICER1 Deficit induces Alu RNA toxicity in age-related macular degeneration. Nature. 2011, 471: 325-330. 10.1038/nature09830.PubMed CentralView ArticlePubMedGoogle Scholar
- Tsirigos A, Rigoutsos I: Alu and B1 repeats have been selectively retained in the upstream and intronic regions of genes of specific functional classes. PLoS Comput Biol. 2009, 5: e1000610-10.1371/journal.pcbi.1000610.PubMed CentralView ArticlePubMedGoogle Scholar
- Shen S: Widespread establishment and regulatory impact of Alu exons in human genes. PNAS. 2011, 108: 2837-2842. 10.1073/pnas.1012834108.PubMed CentralView ArticlePubMedGoogle Scholar
- Lin L, Shen S, Tye A: Diverse splicing patterns of exonized Alu elements in human tissues. PLoS Genet. 2008, 4: e1000225-10.1371/journal.pgen.1000225.PubMed CentralView ArticlePubMedGoogle Scholar
- Smalheiser NR, Torvik VI: Alu elements within human mRNAs are probable microRNA targets. Trends Genet. 2006, 22: 532-536. 10.1016/j.tig.2006.08.007.View ArticlePubMedGoogle Scholar
- Gong C, Maquat LE: LncRNAs transactivate STAU1-mediated mRNA decay by duplexing with 3′ UTRs via Alu elements. Nature. 2011, 470: 284-288. 10.1038/nature09701.PubMed CentralView ArticlePubMedGoogle Scholar
- Grover D, Majumder PPB, Rao C: Nonrandom distribution of Alu elements in genes of various functional categories: insight from analysis of human chromosomes 21 and 22. Mol Biol Evol. 2003, 20: 1420-1424. 10.1093/molbev/msg153.View ArticlePubMedGoogle Scholar
- Muotri AR: The necessary junk: new functions for transposable elements. Hum Mol Genet. 2007, 16: R159-R167. 10.1093/hmg/ddm196.View ArticlePubMedGoogle Scholar
- Carthew RW, Sontheimer EJ: Origins and mechanisms of miRNAs and siRNAs. Cell. 2009, 136: 642-655. 10.1016/j.cell.2009.01.035.PubMed CentralView ArticlePubMedGoogle Scholar
- Tomari Y, Zamore PD: Perspective: machines for RNAi. Genes. 2005, 19: 517-529. 10.1101/gad.1284105.View ArticleGoogle Scholar
- Kumar M, Carmichael GC: Antisense RNA: function and fate of duplex RNA in cells of higher eukaryotes. Microbiol Mol Biol Rev. 1998, 62: 1415-1434.PubMed CentralPubMedGoogle Scholar
- Poliseno L, Salmena L, Zhang J: A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature. 2010, 465: 1033-1038. 10.1038/nature09144.PubMed CentralView ArticlePubMedGoogle Scholar
- Salmena L, Poliseno L, Tay Y: A ceRNA hypothesis: the Rosetta stone of a hidden RNA language?. Cell. 2011, 146: 353-258. 10.1016/j.cell.2011.07.014.PubMed CentralView ArticlePubMedGoogle Scholar
- Vidal F, Mougneau E, Glaichenhaus N: Coordinated posttranscriptional control of gene expression by modular elements including Alu-like repetitive sequences. Proc Natl Acad Sci. 1993, 90: 208-212. 10.1073/pnas.90.1.208.PubMed CentralView ArticlePubMedGoogle Scholar
- Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E: The role of site accessibility in microRNA target recognition. Nat Genet. 2007, 39: 1278-1284. 10.1038/ng2135.View ArticlePubMedGoogle Scholar
- Pruitt KD, Tatusova T, Brown GR, Maglott DR: NCBI reference sequences RefSeq: current status, new features and genome annotation policy. Nucleic Acids Res. 2012, 40: D130-D135. 10.1093/nar/gkr1079.PubMed CentralView ArticlePubMedGoogle Scholar
- Mootha VK, Lindgren CM, Eriksson KF: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003, 34: 267-273. 10.1038/ng1180.View ArticlePubMedGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005, 102: 15545-15550. 10.1073/pnas.0506580102.PubMed CentralView ArticlePubMedGoogle Scholar
- Rubin CM: Selective stimulation of translational expression by Alu RNA. Nucl. Acids Res. 2002, 30 (14): 3253-3261. 10.1093/nar/gkf419.PubMed CentralView ArticlePubMedGoogle Scholar
- Hofacker IL: Fast folding and comparison of RNA secondary structures. Monatsh. Chem. 1994, 125: 167-188. 10.1007/BF00818163.View ArticleGoogle Scholar
- Hofacker ILV: RNA secondary structure server. Nucleic Acids Res. 2003, 31: 3429-3431. 10.1093/nar/gkg599.PubMed CentralView ArticlePubMedGoogle Scholar
- Noe L, Kucherov G: YASS: enhancing the sensitivity of DNA similarity search. Nucleic Acids Res. 2005, 332: W540-W543.View ArticleGoogle Scholar
- Thomas PD, Kejariwal A, Campbell MJ: PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res. 2003, 31: 334-341. 10.1093/nar/gkg115.PubMed CentralView ArticlePubMedGoogle Scholar
- Bolstad BM, Irizarry RA, Astrand M: A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics. 2003, 19: 185-193. 10.1093/bioinformatics/19.2.185.View ArticlePubMedGoogle Scholar
- Irizarry RA, Bolstad BM, Collin F: Summaries of affymetrix GeneChip probe level data. Nucleic Acids Res. 2003, 31: e15-10.1093/nar/gng015.PubMed CentralView ArticlePubMedGoogle Scholar
- Irizarry RA, Hobbs B, Collin F: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003, 4: 249-264. 10.1093/biostatistics/4.2.249.View ArticlePubMedGoogle Scholar
- de Hoon MJ, Imoto S, Nolan J: Open source clustering software. Bioinformatics. 2004, 20: 1453-1454. 10.1093/bioinformatics/bth078.View ArticlePubMedGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci. 1998, 95: 14863-14868. 10.1073/pnas.95.25.14863.PubMed CentralView ArticlePubMedGoogle Scholar
- Saldanha AJ: Java treeview—extensible visualization of microarray data. Bioinformatics. 2004, 20: 3246-3248. 10.1093/bioinformatics/bth349.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.