- Research article
- Open Access
Identification of novel endogenous antisense transcripts by DNA microarray analysis targeting complementary strand of annotated genes
© Numata et al; licensee BioMed Central Ltd. 2009
- Received: 27 February 2009
- Accepted: 22 August 2009
- Published: 22 August 2009
Recent transcriptomic analyses in mammals have uncovered the widespread occurrence of endogenous antisense transcripts, termed natural antisense transcripts (NATs). NATs are transcribed from the opposite strand of the gene locus and are thought to control sense gene expression, but the mechanism of such regulation is as yet unknown. Although several thousand potential sense-antisense pairs have been identified in mammals, examples of functionally characterized NATs remain limited. To identify NAT candidates suitable for further functional analyses, we performed DNA microarray-based NAT screening using mouse adult normal tissues and mammary tumors to target not only the sense orientation but also the complementary strand of the annotated genes.
First, we designed microarray probes to target the complementary strand of genes for which an antisense counterpart had been identified only in human public cDNA sources, but not in the mouse. We observed a prominent expression signal from 66.1% of 635 target genes, and 58 genes of these showed tissue-specific expression. Expression analyses of selected examples (Acaa1b and Aard) confirmed their dynamic transcription in vivo. Although interspecies conservation of NAT expression was previously investigated by the presence of cDNA sources in both species, our results suggest that there are more examples of human-mouse conserved NATs that could not be identified by cDNA sources. We also designed probes to target the complementary strand of well-characterized genes, including oncogenes, and compared the expression of these genes between mammary cancerous tissues and non-pathological tissues. We found that antisense expression of 95 genes of 404 well-annotated genes was markedly altered in tumor tissue compared with that in normal tissue and that 19 of these genes also exhibited changes in sense gene expression. These results highlight the importance of NAT expression in the regulation of cellular events and in pathological conditions.
Our microarray platform targeting the complementary strand of annotated genes successfully identified novel NATs that could not be identified by publically available cDNA data, and as such could not be detected by the usual "sense-targeting" microarray approach. Differentially expressed NATs monitored by this platform may provide candidates for investigations of gene function. An advantage of our microarray platform is that it can be applied to any genes and target samples of interest.
- Microarray Platform
- Antisense Transcript
- Mouse Mammary Tumor Virus
- Complementary Strand
- Antisense Transcription
There is a growing body of evidence that natural antisense transcripts (NATs) play important regulatory roles in various biological processes. NATs are usually transcribed from the opposite strand of a particular gene locus, and they are thought to regulate sense gene expression [1, 2]. One of the proposed models of NAT-mediated regulation is for the antisense transcript to act as a cis-repressor of gene expression from the sense strand. For example, in early embryogenesis, transcription of the antisense genes Tsix and Air determines the fate of expression of their sense partners Xist and Igf2r, respectively [3, 4]. The appearance of NATs within several imprinted loci suggests that NATs may regulate gene expression by controlling the epigenetic status of surrounding genes [5–7]. Moreover, NATs may function in pathological conditions by causing epigenetic alterations such as histone modification and DNA methylation [8, 9].
The other primary model of NAT-mediated gene regulation is induction of the production of small RNAs from NAT loci and their subsequent function in RNA interference (RNAi) pathways. Endogenous small interfering RNA (endo-siRNA) molecules, generated from NAT loci, are induced specifically under conditions of salt stress and immune response in plants [10–15]. Recent experimental data also suggests the presence of NAT-associated endo-siRNA molecules in animals [16–18].
Although the number of NATs thought to have biological functions has gradually increased, the functions of most NATs discovered in recent large-scale in silico studies are unknown. Computational identification of NATs is based mostly on the analysis of cDNA and EST sequence collections by sequence alignment, and this process has identified several thousand sense-antisense pairs . However, in principle, cDNA sequencing accumulates data on transcripts with poly(A)-stretches and does not access the non-poly-adenylated population of transcripts. A recent genome-wide tiling array study of the human genome revealed that many genomic regions that could not be identified from cDNA collections are apparently transcribed and tend not to be poly-adenylated . This finding indicates that antisense transcriptome analyses based solely on cDNA information may be inefficient. In addition, most publicly available cDNA sequences are derived from normal cellular conditions, such as normal adult tissues, and thus are not useful for the identification of NATs specific to abnormal cellular conditions.
To discover novel NATs expressed under various biological conditions, we proposed a microarray-based technique involving the use of 60-mer oligonucleotide DNA probes selected from the complementary sequences of cDNAs (i.e., known genes), referred to as artificial antisense sequence (AFAS) probes. This approach has the ability to detect antisense expression that cannot be identified by using information from the cDNA and EST collections and has the advantage of compatibility with the computational methodology widely used for sense gene expression analysis . We performed microarray analyses with AFAS probes by using oligo-dT and random primed target samples to provide a comprehensive approach for the detection of novel non-poly-adenylated transcripts in the antisense transcriptome.
Here, we designed AFAS probes to correspond to the antisense strand of well-studied selected genes, including oncogenes and tumor suppressor genes, imprinted genes, and human-mouse orthologous genes. We studied the expression profiles of targeted transcripts in normal mouse adult tissues and in mouse mammary tumor virus (MMTV)-induced mammary tumors. This technique is applicable to all genes and sample types and can be used for antisense expression identification that is not possible by using conventional cDNA information alone.
AFAS probes detect previously known NATs
Global analyses of AFAS probes
Before screening for novel NATs using AFAS probes, we first analyzed the global tendency of signal intensities from all AFAS probes applied to our custom microarray platform. Because Northern blot analyses for particular gene loci have previously shown that NATs tend to be poly(A)-negative , we checked whether our AFAS probes also showed this tendency in normal mouse tissue expression profiling. A significantly higher number of AFAS probes than sense probes detected transcripts only within random-primed samples, but not among the oligo-dT primed targets (P < 2.2e-16, Fisher's exact test, see Additional file 3). This result indicates that transcripts detected by AFAS probes also lack poly(A)-tails, similar to the finding for NATs characterized by Northern blot analyses . Also, the number of sense probes detecting transcripts in both oligo-dT and random primed samples was higher than that of antisense probes (P < 2.2e-16, Fisher's exact test, see Additional file 3). This finding indicates that sense transcripts with poly(A)-tails can be identified by both priming methods, because sense probes target the protein-coding strand of the mRNA, which is expected to have a poly(A)-tail. Another characteristic of endogenous NATs is their nuclear localization . A distribution comparison of AFAS probe signals between nuclear and cytoplasmic fractions clearly showed nuclear enrichment of detected transcripts (Figure 1B, P < 2.2e-16, Median test).
Several large-scale studies using information in cDNA and EST collections and from genome-wide tiling arrays in yeast previously showed that NATs tend to be transcribed from the 3' region of its counterpart mRNA [25–27], thus implying the presence of regulatory mechanisms involving tail-to-tail overlapping. We also observed this characteristic for the AFAS probes, because the AFAS probe signals clearly showed positional preference relative to the sense mRNA (Figure 1C). This result indicates that AFAS probes indeed detect the positional bias of antisense transcription. Similarly, we also observed higher signals within 5' regions (Figure 1C), thus suggesting that NATs may also arise near the transcriptional start site, as previously shown for head-to-head overlapping NATs such as WT1, Sphk1, and Tsix [28–30].
Novel conserved NAT detection by normal tissue profiling
We designed AFAS probes corresponding to 635 mouse orthologous partners, for which the antisense counterpart has been identified in humans, but not in mice (one sense and one antisense probe were designed per gene). We then profiled the expression of these genes to detect antisense expression within 12 normal mouse tissues. We identified 420 (66.1%) probes that gave a signal (signal intensity ≥100, which is our empirically defined criterion), at least in a single particular tissue, and 58 of these (9.2%) showed tissue-specific expression. Probes of 120 genes gave signals with a higher than average intensity according to inter-array normalization (see Additional file 4). These results suggest that many NATs identified only in the human cDNA collection may also be expressed in mice.
We attempted to validate the expression of two candidate conserved NATs (antisense of Acaa1b and Aard) by performing Northern and in situ hybridization (ISH) analyses. Whereas human ACAA1 (acetyl-Coenzyme A acyltransferase 1) overlaps with DLEC1 (deleted in lung and esophageal cancer 1) in a tail-to-tail overlapping manner, its orthologous counterpart in the mouse genome (Acaa1b and Dlec1) shows a tail-to-tail relationship but not a reciprocal overlapping relationship, according to the annotated gene structure (see Additional file 5). Both microarray and Northern analyses confirmed that the Acaa1b sense transcript is expressed within liver and kidney (see Additional file 5). Northern analyses were not able to detect the antisense transcript of Acaa1b from either poly(A)+ or total RNA (data not shown), but quantitative RT-PCR, ISH and microarray analyses were able to detect this transcript within the testis and kidney (see Additional file 5). This result implies that NATs detected by microarray analysis using AFAS probes are transcribed in vivo.
These data clearly confirm that AFAS probes can detect the expression of antisense transcripts in normal tissues, and that they can also identify transcripts expressed in a tissue- and cell-type-specific manner. Detection of such expression dynamics for antisense transcripts is possible only by using the analytical platform targeting the complementary strand of the annotated genes. Thus, AFAS probes, when used within appropriate biological samples and combined with other analytical modalities, can be used to discover genuine functional NATs; this is an advantage over conventional approaches that depend on publicly available cDNA data.
Detection of novel NATs differentially expressed under pathological conditions
We next checked whether AFAS probes have the ability to detect antisense transcripts in cancerous tissues. Examples of functional antisense transcripts identified in abnormal cells are CDKN2B, WT1, and HBA2 [8, 9, 29]. These antisense transcripts control the epigenetic status of surrounding genes by DNA methylation or histone modification and thus are thought to affect the expression of their sense partners. To confirm this notion, we applied the AFAS probe technique to the 404 well-characterized genes including oncogenes and tumor suppressors (1752 AFAS probes were successfully designed, giving 4.4 probes per gene on average). We used these probes in microarray experiments based on the GRS/A mouse strain, which frequently suffers from (MMTV)-induced mammary tumors .
This paper shows that microarray probes targeting transcription from the complementary strand of known genes can identify novel NATs, an approach that has not been possible solely on the basis of publicly available cDNA data. Recently described high-density oligonucleotide tiling-array platforms are designed to overview the transcriptional landscape of specific genomic regions at high resolution. By comparison, our platform uses multiple probes to specifically screen for transcription from the antisense strand of known genes. Many previous studies have attempted to identify NATs by DNA microarray analysis using cDNA-oriented custom microarrays or commercially available microarray platforms [37–41]. Since our microarray platform is custom-made and not commercial, it can be applied to any genes or gene loci of interest. Furthermore, our method does not introduce bias from cDNA synthesis between sense and antisense profiling because it does not require specific protocols for target cDNA synthesis for NAT detection. In addition, our microarray platform approach can simultaneously profile sense and antisense expression in one microarray hybridization experiment.
Many NATs detected by AFAS probes were appeared only in the random-primed targets. This was concordance with previous cDNA-based microarray profiling of NAT expression . Whereas poly(A)-plus RNA population is roughly represented by oligo-dT primed cDNAs, whole transcriptome (including the poly(A)-minus RNA population) is represented by cDNAs synthesized by random primers. Therefore, NATs detected by our analysis tend to be poly(A)-negative. Although oligo-dT primers can pick the internal poly(A)-stretches, this is not an issue at the level of microarray-based NAT screening, because the vast majority of the poly(A)-stretch (approximately 90%) is located within the 3' end of the transcripts (data not shown).
By designating AFAS probes to human-mouse orthologous genes, we identified many probes showing positive signals. Two of these probes identified transcripts for which in vivo expression was confirmed. Thus, our approach may reveal more, as yet unidentified, conserved NATs; this has not been possible by conventional approaches, as previously reported using cDNA data [26, 31, 32]. Of the individually validated examples (Acaa1b and Aard), expression of Aard-AS was localized to the nucleus and was detected only in random-primed target samples. In addition, multiple-size hybridized bands pattern was observed especially for total RNA membrane, not for poly(A)+ RNA membrane. This observation is similar to that of previously identified antisense transcripts , and this is probably due to heterogeneously sized molecules of Aard-AS transcripts. Because ISH and the microarray data on other antisense transcript examples also show nuclear localization and poly(A)-avoidance (data not shown), it is possible that these features are general characteristics of the antisense transcriptome.
We also designed AFAS probes for well-characterized genes and identified several examples of correlated and anti-correlated expression between the NATs and the corresponding sense transcript within MMTV-induced mammary tumors. We observed differentially expressed genes for which expression of the antisense transcript had changed, whereas that of the sense transcript had not. Given that differential antisense expression might induce changes in epigenetic status, for example in CDKN2B and CDKN2BAS , antisense transcription may cause changes in the methylation status of neighboring genes. This notion can be tested by using methylated DNA immunoprecipitation (MeDIP) and chromatin immunoprecipitation (ChIP) on chip analyses to further characterize the antisense transcriptome and to determine whether specific NATs function as epigenetic regulators. Whereas this study revealed NATs specific to mouse tumors, human clinical samples have also been analyzed to screen for novel NATs by the same methodology; this new study has identified many antisense transcripts showing increased or decreased expression in human colon cancer tissues compared with controls (Saito R., Kohno K., Okada Y., Osada Y., Numata K., Watanabe K., Nakaoka H., Yamamoto N., Kanai A., Yasue H. et al., manuscript in preparation).
Although next-generation high-throughput transcriptome sequencing (RNA-seq) might replace microarray-based expression analyses, antisense transcriptome analysis by sequencing is still under development because of the laborious nature of strand-specific library construction . DNA microarray-based profiling makes it possible to gain a detailed view of specific genes or gene loci and can also provide expression profiles of both poly(A)-plus and poly(A)-minus RNAs.
We showed here that probes targeting the complementary strand of the annotated genes successfully identify novel NAT expression, including those altered tissue- and tumor-specifically. The results suggest that there are more examples of NATs that cannot not be collected from public cDNA sources. Further functional investigation is required for such dynamically expressed NATs, and the use of microarray platforms targeting both strands of the gene locus will help to narrow down the proper candidates for further functional analyses.
Custom microarray construction
The AFAS probes for detecting NATs were designed to detect antisense transcription originating from genes categorized into three groups: (1) 48 genes in which antisense transcription has been previously reported and 87 imprinted genes in mice, (2) 404 selected well-annotated genes, (3) orthologous genes in NAT loci (detailed definition given below), and (4) randomly selected genes for which there were no cDNA, EST, and CAGE tags in the antisense orientation. For categories (1) and (2), the AFAS probes were designed to correspond to every 500 bases of the antisense strand of the exonic regions of each gene. For category (3), the AFAS probes were designed to correspond to a single specific sequence in each transcript. For category (4), two AFAS probes were designed per transcript. Target region selection for the probe design is summarized in Additional file 8. All probes were computationally designed by using the OligoWiz program  and were used in the Agilent 44K custom oligoarray platform for single-color microarray analysis.
Target sample preparation for the microarray analysis
Total RNA for the mouse (C57BL/6J) microarray experiments was isolated from NIH3T3 cells (fibroblast cell line), SL10 cells (fibroblast cell line), brain, heart, intestine, kidney, liver, lung, placenta (d.p.c. 10.5 and 13.5), spleen, stomach, testis, and thymus. Testis was from C57BL/6J males (8 to 10 weeks), placenta was from pregnant mice, and the other tissue was from both male and female mice. Nuclear and cytoplasmic fractionation of NIH3T3 cells was carried out according to the Protein and RNA Isolation System (PARIS) instructions (Ambion Inc.). For the microarray analysis of murine mammary tumors, RNA samples were collected from normal and cancerous mammary glands of dissected GRS/A mice .
Data processing and the accessibility
Numerical processed signal values (gProcessedSignal) of the Agilent Feature Extraction File were obtained as representative expression levels for each probe within the array. If a spot had an intensity value lower than five, or if there was no prominent difference between foreground and background signals, then the intensity value was adjusted to five and the corresponding probe was treated as an "absent probe". To perform normalization of signal intensity distribution between multiple arrays, the whole mean signal of every hybridization experiment was adjusted to that of the data from SL10 cells by oligo-dT priming. Probes with intensity values lower than five, as well as being flagged as "saturated", were discarded for the inter-array-normalization step. Tissue-specificity of the expression signals was evaluated according to τ measurement . The raw data from the microarray analyses were deposited in the NCBI Gene Expression Omnibus (GEO) under accession number GSE14568 . Expression data as well as a simplified genomic structure can be accessed via an originally constructed viewer .
In silico identification of orthologous genes in NAT loci
To identify orthologous genes in NAT loci (Figure 2), we initially performed in silico identification of sense-antisense pairs by the same procedures as previously published , by using the latest full-length cDNA collections [33, 48], NCBI RefSeq mRNA  and the UniGene collection . This identified 3524 and 5351 exon-overlapping sense-antisense pairs in humans and mice, respectively. Genomic synteny data between human and mouse (defined by BLASTZ derived from UCSC ) was then exploited to determine whether each identified pair was located within the syntenic region between the two species. Those pairs located within the syntenic regions were retained for the orthologous relationship validation. The orthologous relationship between the genes located within the syntenic regions was defined according to the orthologous gene table from the BioMart Project . Finally, 648 genes are identified as orthologous genes for which NAT was identified in human cDNAs but not in mouse cDNAs. AFAS probes for these (635 of 648) were successfully designed.
Northern hybridization analyses
RNA from mouse tissues (C57BL/6J, 8 to 10 weeks, male and female mixed), and the NIH3T3 was isolated by using Trizol reagent (Invitrogen Corporation). Northern analyses were performed as previously described . Loading of equal amounts of RNA samples was confirmed by visualization of ethidium bromide-stained RNA in the gel. Probes specific for sense and antisense of Acaa1b (NM_146230), Aard (NM_175503), and Thbd (NM_009378) were amplified by the PCR (see Additional file 9). All the probe sequences contained their corresponding microarray probe sequences. cDNA fragments were cloned to the pGEM-T Easy Vector (Promega Corporation), and strand-specific cRNA was prepared for hybridization.
In situ hybridization
Probes specific for sense and antisense of Acaa1b (NM_146230), Aard (NM_175503), and Thbd (NM_009378) were amplified by the PCR (see Additional file 9). All the probe sequences contained their corresponding microarray probe sequences. The amplified fragment was sub-cloned into pGEMT-Easy vector (Promega) and was used for generation of sense or antisense RNA probes. Paraffin-embedded testis sections (6 μm) of normal adult mouse (C57BL/6 mouse, male, 8 weeks) were obtained from Genostaff Co., Ltd. For in situ hybridization the sections were hybridized with digoxigenin-labeled RNA probes at 60°C for 16 h. The bound label was detected using NBT-BCIP, an alkaline phosphate color substrate. The sections were counterstained with Kernechtrot (Muto Pure Chemicals Co., Ltd.). Probe sequence of negative control experiment was selected from Oryza sativa putative leaf protein (NM_197207) (see Additional file 5 and 10).
Real-time quantitative RT-PCR
cDNA was initially synthesized with gene-specific reverse primers (Acaa1b-AS and Gapdh) from selected tissue RNA (Brain, Testis, Kidney, and Liver), then subjected to quantitative RT-PCR. Gene expression level was normalized with Gapdh. Primers are listed in Additional file 11.
The authors acknowledge Yutaka Watanabe and Takahiro Doi (RIKEN BRC) for their helpful discussions, Hidemasa Kato (Saitama Medical University) for critical reading of the manuscript, Shinichi Kashiwabara (Tsukuba University) for fractionation of the testis germ cells, and Toutai Mitsuyama (Computational Biology Research Center, Advanced Industrial Science and Technology, Japan) and Kiyoshi Asai (Tokyo University) for bioinformatic support. We also thank Naoto Kaneko (Tsukuba University), Kouichi Tatsuguchi and Yukiaki Kikuta (C's Lab Co. Ltd), members of Genostaff Inc., and staff at Hokkaido System Science Co. Ltd for technical and experimental support. This work was supported in part by grants from the Non-coding RNA Project by the New Energy and Industrial Technology Development Organization (NEDO) of Japan; and by a Research Fellowship of the Japan Society for the Promotion of Science (JSPS) for Young Scientists to K.N.
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