Introduction

Gene expression is tightly regulated at the transcriptional and post-transcriptional levels. Transcription is initiated through recruitment of the transcription pre-initiation complex at a promoter, a proximal upstream region of a transcribed gene1. Multiple promoters of a single gene produce distinct transcripts; five or more promoters per gene are reported2. The variety of transcripts is further extended by co- or post-transcriptional regulation. In particular, in alternative splicing, different sets of introns are cleaved out from primary transcripts3, and three or more isoforms per gene are reported4.

Endometrial cancer is the most common type of cancer of the female reproductive tract in developed countries. Only a small fraction of patients has metastasis in lymph nodes within the low-risk patient group, and no established system is available to identify such exceptional cases. We explored the differences of the exceptional cases in transcription through cap analysis of gene expression (CAGE), a method to quantitatively monitor transcription initiation at base-pair resolution5. Two genes were found to indicate metastasis in lymph nodes6; identification of these molecular markers have opened a way toward preoperative diagnosis. Of them, the gene for transforming acidic coiled-coil-containing protein 2 (TACC2)7 was found to harbor a novel intronic promoter, p10, whose activity is higher in patients with lymph-node metastasis6. The gene encodes a member of the TACC family, which consists of three members that share a conserved C-terminal coiled-coil region, called TACC domain7,8. The gene was originally identified as a tumor suppressor in breast cancer9, whereas later it was reported as a tumor-promoting factor in prostate cancer10,11 and breast cancer12. These reports support association of TACC2 with tumors, but the control of its expression and its effect on tumors remain unclear. Of the eight TACC2 isoforms reported in 200313, three are around 10-kb long as they have a 4th exon of 5.3 kb, and five isoforms are around 5 kb and do not have this long exon; none of them are initiated from the p10 promoter. Capillary sequencing of cDNA clones derived from p10 revealed that their exons are largely consistent with the ones derived from the canonical promoters6. Ten distinct isoforms were found among 16 cDNA clones6, indicating the existence of unknown isoforms.

Recent single-molecule long-read sequencing technologies, such as single-molecule real-time (SMRT) sequencing by Pacific Biosciences (PacBio)14 and nanopore sequencing by Oxford Nanopore Technologies (ONT)15,16, enable us to sequence DNA molecules longer than 10 kb without fragmentation17,18. SMRT sequencers measure fluorescence fluctuations that arise from the addition of a specific nucleotide14, whereas nanopore sequencers (MinION, GridION, and PromethION) measure ionic current fluctuations that occur when single-stranded nucleic acids pass through nanopores15. They make it possible to determine the entire transcript structure without cloning of individual cDNAs. Nanopore sequencing produces longer read lengths with lower sequencing costs, but generates more sequencing errors (about 10%19,20) than HiFi reads of SMRT sequencing that relies on consensus among multiple passages of sequencing through circularized template. A protocol to increase sequence accuracy in nanopore sequencers, called 2D sequencing, which determine nucleotides in both strands of DNA molecules sequentially through ligation of a hairpin adapter, was developed once but not available at this moment. Only the method to sequence a single strand, called 1D, is available currently, and there is a demand to obtain accurate sequences with that.

Here, we aimed to provide a comprehensive view of the TACC2 isoforms derived from the intronic p10 promoter. We used an ONT MinION sequencer to sequence long-range PCR amplicons of the cDNAs, followed by computational error correction. We started by profiling an endometrial cancer cell line (JHUEM-1), and then extended the study to primary tissues of endometrial cancer and normal endometrium. Our results unveiled the diversity of isoforms both in the cell line and primary tissues.

Material and methods

All experiments on human subjects were conducted in accordance with the declaration of Helsinki.

JHUEM-1 cell line culture and RNA preparation

The endometrial cancer cell line JHUEM-1 was provided by the RIKEN BRC through the National Bio-Resource Project of the MEXT, Japan. JHUEM-1 cells were cultured in Dulbecco’s modified Eagle’s medium /HamF12 (Sigma-Aldrich Corp., St. Louis, MO, USA) supplemented with 15% fetal bovine serum and 1% penicillin–streptomycin solution. The cells were plated in 6-cm dishes at 105 per dish and incubated for 6 days.

The culture medium was removed by aspiration, the cells were washed with phosphate-buffered saline (PBS), which was removed by aspiration and replaced with PBS containing 0.2% trypsin. Cells were transferred to an RNase-free tube, medium was added, and centrifuged at 300×g for 5 min. The supernatant was completely removed by aspiration. Total RNA was extracted with an RNeasy Mini Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s protocol. Total RNA was diluted to 250 ng/μl based on Nanodrop measurements.

Patients, sample collection, and RNA preparation

Endometrial cancer tissues and normal endometrium tissues from patients with benign gynecological tumor were obtained from the patients recruited from the Department of Obstetrics and Gynecology, Juntendo University Hospital, Tokyo, Japan, with a written informed consent by following a protocol approved by the ethical review board of Juntendo University Faculty of Medicine. For classification of clinical cases, the patient's information such as histologic subtype, differentiation grade, the International Federation of Gynecology and Obstetrics (FIGO) stage in 2008 and TNM classification were used.

Cancer tissues were cut into 5 mm cubes after hysterectomy, frozen immediately in liquid nitrogen, and stored at − 80 °C. Frozen samples were cut into 30 mg (~ 3 mm cubes) and RNA was extracted with an RNeasy Mini Kit. Total RNA was eluted with 50 µl of RNase-free water. Normal endometrium tissues were cut into 2 mm cubes after hysterectomy, and immediately placed into a PAXgene Tissue FIX Container (Qiagen) in liquid-I (Tissue Fix) at room temperature; after 3 h, they were transferred into liquid-II (Stabilizer) and stored at − 80 °C. RNA was extracted according to the Qiagen protocol for the PAXgene Tissue Container. Total RNA was eluted with 28 µl of RNase-free water. Total RNA was diluted to 250 ng/μl based on Nanodrop measurements.

Preparation of sequencing target amplicons

cDNA was prepared from total RNA of JHUEM-1 cells and surgical specimens using a PrimeScript II 1st strand cDNA Synthesis Kit (Takara Bio Inc., Kusatsu, Japan). Briefly, samples were denatured at 65 °C for 5 min in the presence of Oligo dT Primer and reverse transcribed at 42 °C for 60 min. The enzyme was inactivated at 70 °C for 15 min, and the samples were immediately chilled at 4 °C. The cDNA was purified with AMPure XP beads (Beckman Coulter, Brea CA, USA), eluted in 50 µl of RNA-free water and 1 µl of the 50 µl was used to prepare TACC2 p10 amplicons by PCR with LA-taq DNA polymerase and GC Buffer. TACC2 p10 forward primer (CCAGTTGCTGAAGGGCAGAA) and TACC2 p10 reverse primer Ex22 (TTGCCTCGAACCTGAGCAATC) were designed from the transcription start site (p10) revealed in a previous study6. Thermal cycling was performed for 30 cycles (98 °C for 10 s, 55 °C for 15 s, and 72 °C 60 s). TACC2 p10 amplicons were purified with AMPure XP beads and eluted with 50 µl of RNA-free water. Their concentration was measured using a Qubit dsDNA BR assay kit (Thermo Fisher Scientific, Waltham, MA, USA) and adjusted to 500 ng per 22.5 µl (22.2 ng/µl). Because the amplicon yield from surgical specimens was low, we performed a second PCR with 1 µl of the 50 µl eluent of TACC2 p10 PCR amplicon as a template for 15 cycles with the same condition, purified the amplicons and adjusted their concentration as above.

Nanopore sequencing

Nanopore sequencing libraries were prepared according to the manufacturer’s protocol for 1D Native barcoding genomic DNA using EXP-NBD103 and SQK-LSK108 (ONT, Oxford, UK). The library of JHUEM-1 amplicons was labeled with the NB01–NB03 barcodes, quantified with a Qubit dsDNA HS assay kit (Thermo Fisher Scientific), and 280 ng was used for sequencing. The library of clinical tissue amplicons was labeled with NB01–NB12 barcodes, quantified as above, and 250 ng of amplicons was used for sequencing. Sequencing was performed on MinION MK1b device with MinION Flow Cell R9.4. MinKNOW software version was 1.5.12. Base calling was performed using Albacore ONT Sequencing Workflow Software v2.3.1 with the Basecall Barcoding workflow (ONT).

Computational processing of MinION read data

We first aligned the reads with the reference genome GRCh37 by using minimap221 with the splice alignment mode option (− x splice). The exon blocks were generated by merging overlapping exons of the primary alignments, and the primary alignments with the same set of exon blocks were grouped. Sequencing errors were corrected with Canu v1.722 by specifying nanopore read (-nanopore-raw) and with additional options corOutCoverage = 999, corMinCoverage = 0, and stopOnReadQuality = false. The corrected reads were subjected to a second round of alignment and grouping. The resulting groups in the second round were expected to agree with individual isoforms more precisely than the ones generated in the first round, as the genome alignments were based on the corrected reads within each of the loosely defined groups in the first round. Within each of the relatively accurate groups generated in the second round, the sequence error correction was conducted again on the original raw reads to avoid bias potentially introduced in the first round owing to incorrect grouping. The script of this error correction step is available as https://github.com/hkawaji/ggec.

The corrected reads were aligned with minimap2 with splice alignment mode (− x splice) and k-mer size 12 (− k 12), and only the primary alignments overlapping with the exons targeted by the amplification primers were kept. Individual exons of the alignments were merged when they overlapped, resulting in 29 non-overlapping merged blocks. The most frequent boundary of the overlapping exons in each block was chosen as the representative exon boundary. Variations of exon boundaries were taken when more than 5% of exon-containing reads were found. Only the alignments matching the representative exon or the identified exon variants were used in isoform counts (Table S4).

Validation PCR and electrophoresis of target amplicon from JHUEM-1 RNA

cDNA was prepared as described above for the JHUEM-1 cell line, diluted tenfold with RNase-free water, and used as a template for PCR in an ABI 7500 Fast Real-time PCR System (Thermo Fisher Scientific) according to the manufacturer’s instructions. The final concentrations in PCR mixture were as follows: 1× Prime STAR Buffer (Mg2+plus), 200 µM dNTP mixture, 0.025 U/µl of PrimeSTAR HS DNA polymerase, 0.52 × SYBR Green (Thermo Fisher Scientific), 0.2 µM forward primer, 0.2 µM reverse primer, and 25 ng/µl cDNA. Thermal cycling was performed for 40 cycles (98 °C for 10 s, 55 °C for 15 s, and 72 °C 60 s). Custom primers for detecting the new exons (Ex2-3, Ex3-4, Ex7-8) were designed by the Primer3 web tool (http://bioinfo.ut.ee/primer3-0.4.0/) and were synthesized by Thermo Fisher Scientific; they are listed in Table S4.

To confirm the amplification with the PCR primers, control synthetic DNA fragments were used (Eurofins Genomics Co., Ltd., Brussels, Belgium). One negative control fragment contained only known TACC2 exons without novel exons, and three positive control fragments contained novel exons (Fig. S5). The design of the forward primer for rex01 (Ex1-2) and the reverse primer for rex05 (Ex3) has been already published6, and these primers were used to amplify the synthetic DNA fragments to assure their quality (Table S5). The new exon custom primers were verified using these control synthetic DNA fragments. The PCR fragments were electrophoresed in 3% NuSieve GTG agarose gel with Tris–borate-EDTA (TBE) buffer for 45 min; 100-bp DNA ladder (Takara Bio, Otsu, Japan) was used as a molecular weight marker. The gel was stained with ethidium bromide to visualize the amplicons. Images were captured using Gel Doc XR plus (Bio-Rad, Hercules, CA, USA).

Results

The intronic p10 promoter of TACC2 is highly active in an endometrial cancer cell line, JHUEM-1

Using RNA-seq data from the Cancer Cell Line Encyclopedia (CCLE)23,24, we explored 25 endometrial cancer cell lines with the active p10 promoter of the TACC2 gene (Table S1) and found that some of them have an RNA-seq signal in the first exon adjacent to the p10 promoter. The expression was most active in JHUEM-1 cells (Fig. S1). The promoter activity was also supported by an unamplified CAGE profile25 in the FANTOM5 project2 (Fig. 1A,B). We chose the JHUEM-1 cell line for the subsequent analysis.

Figure 1
figure 1

The intronic promoter p10 of the TACC2 gene is active in the endometrial cancer cell line JHUEM-1. (A) Genomic view of the transcriptome (RNA-seq and CAGE) of the TACC2 locus with known isoforms. Turquoise background indicates the same region of p10, pink and red bars indicate signal intensities of RNA-seq and CAGE. Black and blue boxes indicate exons of gene models previously reported and included RefSeq database. (B) Close-up view of the intronic promoter region. (C) Schematic representation of our experiments.

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Amplicon sequencing of RNA isoforms transcribed from the intronic promoter

To obtain the full-length amplicons of TACC2 isoforms, we cultured JHUEM-1 cells, extracted RNA, and performed long-range PCR with a primer pair amplifying transcripts from the first exon immediately downstream of p10 to the penultimate exon (Fig. 1C). Using nanopore sequencing, we determined the entire structures of the TACC2 RNA isoforms transcribed from the p10 promoter.

We found two distinct sizes of amplicons (Fig. S2A), suggesting a subset of the isoforms with the long 4th exon13. We performed long-range PCR in triplicate, ligated an adaptor with a barcode sequence unique to each replicate, pooled the replicates, and subjected them to nanopore sequencing. We obtained 869,847 reads in total; their size distribution was consistent with the electrophoresis data (Table S2; Fig. S2).

Sequence error correction and exon identification

Despite the advantage of read length in nanopore sequencing, its drawback is its high error rate (about 10%19,20), which makes it challenging to accurately determine exon boundaries through their alignment with the genome. Previous studies using ONT sequencing of long-range PCR amplicons relied on 2D pass reads26,27, but we could not use their computational pipelines because our sequencing was based on 1D. Error correction approaches are based on two strategies28. The “hybrid” strategy relies on accurate short reads28, where additional data has to be obtained separately and correction prioritizes sequences shared among abundant isoforms. This could skew the data toward major isoforms and impede identification of the minor ones. “Self-correction” relies on the other reads obtained from the same experiment29, and its direct application to RNA isoforms may still result in skewing the data toward abundant isoforms. We developed an alternative strategy, called “reference-guided self-correction”. The long-read sequences are grouped on the basis of loosely defined exon structures and are subjected to self-correction (Figs. 2A,B, S3). As the correction strategy requires the reads to be aligned with the genome and to belong to a group with multiple members, only a subset (365,177, ~ 40%) of the reads were corrected. We found a fraction of reads derived from other gnomic regions due to non-specific hybridization of the PCR primers, and ones which ends do not necessarily matched to the priming site probably due to incompleteness of error correction. We selected 177,369 (~ 20%) aligned reads that cover the genomic regions corresponding to the primers used for amplification (Table S2). Of them, ~ 4% entirely matched to combinations of 29 representative exon boundaries and their variations defined below.

Figure 2
figure 2

Sequence error correction and identified exons. (A) Schematic representation of sequence error correction. Individual lines with the same color indicate the same isoform, where “x” indicates sequence errors (B) Sequence matches and mismatches in the alignment before and after sequence error correction, shown in IGV. The pink and blue regions indicate forward and reverse aligned blocks between the reads and the genome. A minor exon, comprising a Gencode gene model ENST00000491540, is shown (referred as rex04). (C) The 29 identified exons: rex01 to rex29. Novel exons are colored by red.

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By aligning the error-corrected reads with the reference genome and examining the aligned blocks, we identified 29 regions and their representative boundaries with the highest frequency per region (Table 1; Figs. 2C, S4). Hereafter, we refer to them using the prefix “rex” for “representative exon regions”, i. e. rex01 to rex29. The outer boundaries of the first and last regions were based on the known gene models, as their internal sequences were the targets of amplification with the PCR primers. Of the 29 regions, 24 exactly matched (at the base-pair level) the exons of the gene models in RefSeq30, Ensembl31, and a previous report6. This confirmation of the known exons indicated the accuracy of our results. We also found three regions, rex04, rex06, and rex12, that did not overlap any known exons, which we considered as candidate novel exons. The remaining two regions were partially overlapping exons of a pseudogene annotated in Ensembl and a transcript model of an EST (expressed sequence tag)-based prediction (SIB Gene in the UCSC Genome Browser Database32, https://ccg.epfl.ch/tromer/). We also found length variations in 13 regions (Table 2); in all of them, either the 5′ or 3′ boundaries were consistent with the representative boundaries. The frequencies of exons and isoforms are summarized in Tables S3 and S4.

Table 1 Representative exon boundaries identified by long-read sequencing.
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Table 2 Variations of exon boundaries.
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Validation of the three novel exons in an endometrial cancer cell line

We examined whether the three novel regions represent authentic exons or technical artifacts arising from sequencing errors. In an independent experiment, we conducted PCR analysis with primers specific to the sequences of the novel regions (Fig. 3). The designed primers targeted the most frequent exon–exon junctions (Figs. 4A, S5, and Table S5). cDNAs prepared from JHUEM-1 cells, and synthetic positive and negative controls were subjected to PCR (Table S6). A single band of the expected size was found in the JHUEM-1 cell sample and in the positive but not negative control for each amplicon (Fig. 4B). These results confirmed the three regions as authentic exons.

Figure 3
figure 3

Frequencies of exon–exon junctions of the three novel exons. Black boxes, known exons; red boxes, novel exons. The connections between exons are depicted as arches; frequencies are shown by arch width and numbers.

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Figure 4
figure 4

PCR amplification of novel exons. (A) Schematic representation of PCR amplicons. (B) Electrophoresis of the PCR amplicons. PC and NC indicates synthesized positive and negative controls shown in Table S6. (a)–(c) indicate the amplicons in (A). Full-length image of the gel is included as Fig. S11.

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Long-read sequencing of RNA isoforms in primary tissues

Next, we analyzed 16 cases of endometrial cancer and 5 normal endometria (Table S7), as transcripts from the intronic promoter p10 of TACC2 were detectable in normal endometria at a similar level to lower cases of primary tumors. We used the same approach as above, but performed two PCR rounds to obtain enough material for sequencing because the RNA amount was limited. We also found two distinct sizes of amplicons (Fig. S6), but the bands corresponding to the larger isoforms were sometimes faint. This is likely due to less efficient amplification of long molecules with the additional round of PCR. We obtained two pools of full-length cDNA amplicons; each of them consisted of 12 samples with unique sequence adaptors (Table S7). Sequencing of the amplicons produced almost ~ 5.5 million reads in total (~ 231 thousand reads on average per sample). We corrected sequencing errors as above and obtained ~ 1.7 million corrected reads (~ 72 thousand on average per sample; ~ 30%; Table S7). The ratio was lower than for JHUEM-1 cells (~ 40%) but the number of corrected reads was larger than in the individual JHUEM-1 replicates. Approximately 179 thousand reads (~ 8 thousand on average per sample; ~ 3%) matched exactly the exons defined above. Although we obtained the full-length sequences of TACC2 from the primary tissues, quantification of isoform abundance was limited because of the additional round of long-range PCR.

Diversity of isoform structures

Our full-length sequence data contained 27 profiles, comprising the triplicates for the cell line and 21 primary tissues, including three duplicates for primary tumors. Approximately 200 thousand full-length error-corrected sequences (8 thousand sequences per profile on average; Tables S2 and S7) were obtained, with each genome alignment exactly matching the 29 representative exons or their 21 variations.

We confirmed 9 of the 10 published isoforms6; 6 isoforms were within the top 10 by the number of reads. The six ones were supported with more than 6000 reads, 2 within the top 50 with more than 500 reads, and one was at the 240th place with 20 reads (Tables S4 and S8. The only missing isoform was expected to have a 60-bp exon, where the exon itself (rex03) was found in our data set with less than 1% frequency (Table 1).

We next assessed the reproducibility of isoform frequencies based on the read counts of technical replicates. In JHUEM-1 cells (Fig. S7), technical replicates rep1 and rep2 were more consistent with each other (Spearman correlation coefficient (rs) = 0.88) than with rep3 (rs = 0.78). On the other hand, in primary tumors, correlation was poor (rs < 0.5 in the examples shown in Fig. S8). Principle component analysis of isoform frequencies (Fig. S9) did not show clear segregation of sample types, while one could assume cell or tissue specific patterns in an analogous way to tissue-specific alternative splicing3. The accuracy of isoform quantification was good for the cell line but limited for primary tissues, probably because of an additional amplification step of long-range PCR, and it is not practical to compare isoform frequencies among different samples by using the present data.

Therefore, we focused on qualitative features rather than quantification. The overall frequencies of isoforms, exons, and exon–exon junctions, and isoform structures are shown in Fig. 5. The isoforms were ranked by frequency, as it provides supporting evidence of the isoform presence. The most frequent one corresponded to only 10% of the reads, suggesting that there are no dominant isoforms; 26 isoforms accounted for 80% of the reads and 101 isoforms accounted for 95%. Notably, the top isoforms show high frequencies in the profiled samples, while the quantification accuracy is limited. To cover all known and newly discovered exons, isoforms down to the 157th isoform were required (Fig. 5). This is consistent with the observation mentioned above that the 240th isoform matched a known isoform.

Figure 5
figure 5

Isoforms transcribed from the TACC2 intronic promoter. Exon structures of 157 isoforms, where the validated novel exon rex12 firstly appears in 157th, are indicated by gray or pink boxes in the main (left bottom) panel. Isoform frequencies are shown by box plots in the middle panel, where frequency is calculated through dividing the number of corrected reads corresponding to the isoform by the total corrected reads per profile. The isoforms are ordered from the top by their total of corrected reads, the number of supporting evidences. Cumulative distribution is shown in the right panel.

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The exon frequencies indicated the presence of three exon classes: 16 stable exons present in nearly all isoforms, 4 variable exons present in more than 10% of the reads, and 9 rare exons present in as few as 0.01–10% of the reads. The three novel exons belonged to the class of rare exons. The functionally characterized TACC domain is located in the C-terminal part of the protein, and all exons encoding this domain were stably included. We found that the variations arise from the N-terminal part, which is similar to that in TACC2 isoforms transcribed from the canonical upstream promoters. The top 12 isoforms arose only from the combinations of variable exons, while a variant of the 6th residential exon lacking 12 bp from its start position appeared repeatedly in the subsequent isoforms (Fig. S10).

Discussion

Here we used nanopore sequencing to extensively profile TACC2 isoforms derived from the intronic promoter p10. We chose nanopore sequencing because of low installation costs, simple operation, and use in other studies in which 2D sequencing was performed26,27 (although we used 1D sequencing). Because this approach has a higher error rate than 2D sequencing or circular consensus sequencing of PacBio (an improved version of SMRT sequencing), we developed a strategy called “reference-guided self-correction” to correct errors in each isoform-level group. This strategy substantially increased base accuracy and reduced the number of available reads to only 3% to 4% of the original reads. Unique molecular identifier (UMI), random oligonucleotide sequences uniquely attached to individual PCR templates, is successfully applied to increase sequence accuracy in a recent study33. It performs error correction within the reads obtained from the same PCR template while our strategy performs the correction within the same isoform. The two strategies approaching the same problem with different levels can be used complementary. UMI-based approach could be applied to a larger potion of the reads as it does not require the reads to be mapped on the genome, and remaining reads with insufficient error-correction at molecular level could be corrected at isoform level. Further, our strategy of isoform-level error correction would be useful for PCR-free applications, such as RNA direct sequencing.

We confirmed two major sizes of TACC2 isoforms, ~ 4 kb and 10 kb (Fig. S2), all previously reported exons6 (Fig. 2; Table 1), and 9 of 10 reported isoforms6 (Fig. 5). Precise matches of the identified exon boundaries to the reported ones emphasize the accuracy of our results at the base-pair level. The absence of the 16th exon in one of the isoforms derived from the most upstream promoter was also consistently found in the previous study6 and our data. The 15th exon was absent in 10 isoforms in the previous study6, and its frequency was lower than 1% in our data set (referred to as rex23; Fig. 5, Table S3). The isoform missing from our data includes a rare exon (rex03, < 1% frequency). It is likely to be absent in the samples we studied, or to be present below the detection limit. We further uncovered three novel exons, which was validated with PCR independently to the sequencing. Overall, we identified 29 exons, and all of them are supported by previous reports, existing databases, and our own confirmatory experiments.

Our data demonstrate strong preference of exon usage: 16 stable, 4 variable, and 9 rare exons. The longest exon (rex08) is variable; it contributed to both bands on the gel electrophoresis of full-length cDNAs. Exons rex09, rex10, and rex11 are also variable. If the four variable exons were chosen randomly, 16 (= 24) isoforms could be present with high frequencies, but only 12 isoforms were present with top frequencies, indicating unrevealed regulation of exon selection. These four consecutive exons span almost 50 kbp on the genome (Fig. 2), and repressive regulation at the post-transcriptional level rather than co-transcriptional regulation seems more likely.

Rare exons also contributed to isoform diversity. Eight of 9 (88%) rare exons and all 4 variable exons were symmetric (i.e., their nucleotide numbers were multiples of 3), whereas only 2 of 16 (12%) stable exons were symmetric (Table 2). This is consistent with the reported preference of alternative exons34. Notably, the regions encoded by the variable and rare exons were located N-terminally of the functionally characterized and cross-species conserved region, TACC domain35. As they do not disrupt the coding frame, the C-terminal domain is preserved in most of the isoforms.

With the 29 identified exons and their 21 variations, we found 1,464 isoforms, including those observed only once, and 240 isoforms were supported by at least 20 reads. The 240th isoform has been independently identified by cDNA cloning with capillary sequencing6. We consider those observed at least 20 times as high-confidence isoforms. If we set the threshold at 3 times per isoform, we found 694 isoforms. Our data unveiled 240 isoforms with high confidence and additional 454 with minimum evidence. Isoform diversity in conjunction with the frequency of exons and exon–exon junctions is shown in Fig. 5.

Diverse isoforms were identified for BRCA127 and CACNA1C26. In the case of BRCA1 having ~ 24 exons, 32 isoforms were identified, including 18 novel ones found in the control lymphoblastoid cells but not in breast cancer tissues. These include aberrant isoforms that are usually degraded by nonsense-mediated RNA decay, upon inhibition of the decay by culturing cells with cycloheximide. The latter study26 identified 241 isoforms and 38 novel exons in the CACNA1C gene, where known isoforms consist of ~ 50 exons. The large number of exons facilitates the great diversity of isoforms. In the TACC2 gene, we found 240 isoforms with high-confidence and 694 isoforms with minimum evidence, based on 29 exons including 3 novel ones. Our data demonstrates that isoform diversity can be increased even with a limited number of exons.

Not all the data on the association of TACC2 expression with tumors are consistent. Chen et al.9 reported it as a tumor suppressor in breast cancer, but Cheng et al.12 reported its oncogenic effect in the same type of cancer. Takayama et al.10,11 suggested its positive effect on tumor growth in prostate cancer. While we did not find any relationships between isoform abundance and cell types, probably because of technical limitations of this study, the discordant reports could be caused by the complexity of gene structure and isoform diversity. Concordant results of tumor associations could be obtained through careful discrimination of these isoforms.