Refining transcriptional programs in kidney development by integration of deep RNA-sequencing and array-based spatial profiling
- Rathi D Thiagarajan1Email author,
- Nicole Cloonan1,
- Brooke B Gardiner1,
- Tim R Mercer1,
- Gabriel Kolle1,
- Ehsan Nourbakhsh1,
- Shivangi Wani1,
- Dave Tang1,
- Keerthana Krishnan1,
- Kylie M Georgas1,
- Bree A Rumballe1,
- Han S Chiu1,
- Jason A Steen1,
- John S Mattick1,
- Melissa H Little1 and
- Sean M Grimmond1Email author
© Thiagarajan et al; licensee BioMed Central Ltd. 2011
Received: 24 March 2011
Accepted: 5 September 2011
Published: 5 September 2011
The developing mouse kidney is currently the best-characterized model of organogenesis at a transcriptional level. Detailed spatial maps have been generated for gene expression profiling combined with systematic in situ screening. These studies, however, fall short of capturing the transcriptional complexity arising from each locus due to the limited scope of microarray-based technology, which is largely based on "gene-centric" models.
To address this, the polyadenylated RNA and microRNA transcriptomes of the 15.5 dpc mouse kidney were profiled using strand-specific RNA-sequencing (RNA-Seq) to a depth sufficient to complement spatial maps from pre-existing microarray datasets. The transcriptional complexity of RNAs arising from mouse RefSeq loci was catalogued; including 3568 alternatively spliced transcripts and 532 uncharacterized alternate 3' UTRs. Antisense expressions for 60% of RefSeq genes was also detected including uncharacterized non-coding transcripts overlapping kidney progenitor markers, Six2 and Sall1, and were validated by section in situ hybridization. Analysis of genes known to be involved in kidney development, particularly during mesenchymal-to-epithelial transition, showed an enrichment of non-coding antisense transcripts extended along protein-coding RNAs.
The resulting resource further refines the transcriptomic cartography of kidney organogenesis by integrating deep RNA sequencing data with locus-based information from previously published expression atlases. The added resolution of RNA-Seq has provided the basis for a transition from classical gene-centric models of kidney development towards more accurate and detailed "transcript-centric" representations, which highlights the extent of transcriptional complexity of genes that direct complex development events.
KeywordsRNA-Seq kidney development microarray Six2, Wt1 sense-antisense transcripts alternative splicing mesenchymal-epithelial transition miR-214, microRNA
The mammalian kidney is a remarkably complex organ at the cellular and functional level, being essential not merely for excretory functions but also for a variety of hormonal and homeostatic regulatory functions. A key structure is the nephron, which represents the functional excretory units of the kidney. During kidney development, the nephron arises via a reciprocal interaction between a mesenchymal progenitor population and an adjacent epithelial ureteric tip, where the latter induces the former to undergo a mesenchymal-to-epithelial transition (MET), signaling the start of nephrogenesis (reviewed in [1, 2]). Although well studied, the complete transcriptional regulatory networks are just beginning to be elucidated.
Transcriptional profiling of the developing kidney using microarrays coupled with RNA in situ hybridizations (ISH) have provided a detailed view of gene expression networks driving developmental processes [3–6]. Despite these advances, microarrays cannot capture the entire transcriptional output from mammalian genes (reviewed in [7, 8]) as they require a priori assumptions about the portion of the genome that is expressed, limiting the ability to use this technology for uncharacterized gene or transcript discovery . This also applies to mRNA variants. On average, 6-7 different mRNA variants can arise from a single active locus , and this complexity includes alternate promoters, alternate 3' untranslated regions (UTRs), alternative exons, and alternative splice sites. The vast majority of this complexity is invisible to microarray probes, which are typically short (25-70 nt) and located in the 3' UTR of transcripts . Such limitations mean that kidney developmental programs have only been explored at "gene-centric" resolution. Given the consequences of transcriptional complexity (alternate domain content, differential transcription factor binding sites and microRNA binding sites from alternative promoter and 3'UTR usage, respectively), understanding the complete repertoire of transcripts is crucial for accurate modelling of kidney organogenesis.
Massive-scale sequencing of transcriptomes (RNA-Seq) overcomes most of the limitations imposed by microarrays, and additionally offers high dynamic range, increased accuracy, and increased specificity [11–13], although not yet capable of single cell resolution. Application of this technology has enabled the identification of uncharacterized transcripts, genes, and non-coding RNAs (ncRNAs) [11, 12, 14, 15], and in all studies, the level of complexity has been far higher than previously predicted. Although these features make it highly desirable, RNA-Seq is not practical for all experiments, due primarily to laborious protocols and the need for large quantities of starting material. The recent application of single-cell RNA-Seq has allowed profiling of samples with limited quantities of sample such as embryonic development, but this technique did not discriminate strand-specific transcripts and did not detect 5' ends of transcripts longer than 3 kb which would hinder analysis of alternative promoter usage [16, 17]. For the analysis of complex processes such as organogenesis where individual cellular components are difficult to separate, RNA-Seq to this level of resolution is not practical whereas gene expression profiling on whole organs may fail to detect subcompartment specific transcripts. The integration of both types of analyses, however, may overcome the limitations of each, without the need of completely replacing current wealth of high-quality microarray datasets.
In this study, we describe a high quality, stranded, polyadenylated RNA-Seq and microRNA (miRNA)-Seq profiling resource of the whole embryonic mouse kidney for the purpose of integrating with previously defined spatial resolution kidney microarray. In comparison to the microarray kidney atlas , we show that high coverage whole organ RNA-Seq is sensitive enough to both detect compartment-specific transcripts, and quantify transcript abundance relative to the whole organ. We have used this technique to assess the transcriptional complexity within the developing kidney subcompartments, identifying mRNA variants of many key kidney developmental genes. We also detect wide-spread sense-antisense transcription among important MET regulators, which we validated by SISH. Together, the datasets generated in this study advance gene-centric models of kidney development pathways towards more complete transcript-centric models, capturing the transcriptional landscape of gene expression.
Deep sequencing of the 15.5 dpc mouse kidney
RNA-MATE and Galaxy tag mapping distribution
Total tags mapping to genome (mm9)
Total unique tags
Number of RefSeq genes (> 1RPKM)
Number of transcripts (> 1RPKM)
Unique tags matching RefSeq NM exons
Unique tags matching consensus gene exons models (RefSeq, Aceview, Ensembl, UCSC genes)
Total unique junction tags
Total unique miRNA tags
Quantifying embryonic kidney locus activity
Detecting rare, tissue-specific transcripts
A major concern of whole organ profiling is the inability to detect rare, cell-type specific transcripts due to the heterogeneity of tissue composition . In the previously described microarray kidney atlas, this was addressed by profiling individual kidney subcompartments . Subcompartment specific transcripts from that kidney microarray atlas were used to determine the sensitivity of tissue-specific transcript detection in whole organ RNA-Seq. As many as 99.7% of all transcripts attributed to major kidney subcompartments were detected, where the remaining discordant probe-sets were prone to cross-hybridizations as noted by probe-set ID suffixes (_s_at, _x_at, and a_at_ ) or generally had low raw signal (below 100 Raw Fluorescent Units) and therefore may be affected by background signal.
In addition, subcompartment-specific transcripts provided the framework to estimate the overall distribution of expression within kidney subcompartments. As shown in Figure 1B, all major kidney subcompartments were represented, where the mean expression abundance for each compartment was between 1-10 RPKM. Rare (0.5 RPKM), subcompartment-specific transcripts detected by the kidney microarray atlas were also identified by RNA-Seq. This confirms that with sufficient sequencing depth, whole organ RNA-Seq can be used to detect gene expression that are representative of specific kidney cellular populations.
Integration of RNA-Seq with spatially-resolved Affymetrix microarrays
Extensive use of extended 3'UTRs in embryonic kidney subcompartments
The 3' UTR contains cis-regulatory elements important for mRNA stability, degradation, subcellular localization and translation. Therefore, accurate characterization of 3'UTR boundaries can help identify key regulatory elements, such as microRNA (miRNA) binding sites. In order to identify expression beyond currently annotated 3'UTR boundaries, we used a sliding window to survey contiguous signal within a 20 kb radius from the annotated 3' end (excluding regions overlapping known RefSeq transcripts including ncRNAs). This approach identified over 1500 genes with 3'UTRs that extend well beyond the mouse RefSeq boundary. Extended UTR sequence genomic coordinates identified by RNA-Seq were obtained from mm9 using Galaxy  to determine if such events were novel or due to incomplete annotations. We found that 720 instances of these extended UTRs have been seen in RefSeq orthologs, often as part of the transcript of genomes with more complete annotations such as human RefSeq (hg18). Overall we find 532 transcripts with previously unannotated 3'UTR extensions, demonstrating the widespread nature of this transcriptional event in the embryonic kidney (Additional file 3).
We then asked whether extended 3'UTR expression was prevalent among genes critical for kidney development by focusing on genes involved during mesenchymal-epithelial transition (MET), which is a critical process for nephron development. Extended UTR expression was detected within the Lhx1 locus, a critical transcriptional regulator of nephron endowment [25, 26] (Figure 2). A ~1.5 kb signal beyond the RefSeq annotated 3' end was detected and represented by probesets 1421951_at (canonical 3'UTR based on RefSeq models) and 1450428_at (extended UTR) with high concordant expression (Pearson correlation R = 0.932). Section ISH (SISH) also confirmed the concordant expression between the extended 3'UTR and the remaining portions of the transcript, localized to the nephron precursor structures (renal vesicle, s-shaped body and nephron tubules) (Figure 2C, D). SISH data and detailed annotations are available at . Studies have described miRNA binding sites for miR-30 within the extended region of Lhx1 3'UTR, where miR-30 inhibits Lhx1 expression and therefore embryonic kidney differentiation  (Figure 2C). This region overlaps with the extended signal detected in our RNA-Seq data, highlighting the importance of accurate representation of gene boundaries.
Alternate exon usage associated with key kidney development loci
Large scale identification of alternative splicing is an essential pre-requisite that will facilitate important downstream functional characterization on how genes are regulated in a tissue-specific manner and the roles of alternate isoforms during developmental states. Alternative splicing can alter mRNA through a variety of mechanisms, including the addition and removal of exons, thereby affecting protein functional domain composition . To identify the presence of isoforms associated with alternate exon usage, reads were mapped to a predefined library of known exon junctions sequences, as described in [11, 30]. Results from the mapping revealed 3568 loci (> 1 RPKM) where alternate exon-junctions were detected (Additional file 4).
Transcriptional complexity and discovery across regulators of kidney development
Known variants (mouse/human)
Variant Junction Location
Supporting transcript models
Number of tags
Skip exon 4 &5
Wt1 -exon 5
Skip exon 5
Isoform A (long) & B (short)
Isoform A & B
Isoform 2 & 3
Temporo-spatial loci with uncharacterized 5' exons and alternative promoter signal
Sequencing of embryonic kidney miRNAs
MiRNAs are short, non-coding species of RNA (~22nt) that function as translational repressors of target mRNAs during many biological processes including development, differentiation, cell proliferation and disease [42, 43]. Within the kidney, tissue-specific knockout of Dicer, an enzyme required for miRNA biogenesis, has previously been reported to alter anatomical organization and to also play a role in renal diseases [44–46]. Identification of the complete miRNA repertoire in the embryonic kidney will serve as an important reference of developmentally regulated miRNAs for functional characterization. To catalogue active miRNAs within the developing mouse kidney, we have isolated and sequenced the small RNA fraction (SOLiD, Applied Biosystem) and mapped the reads against the entire miRBase (v15) database . This provided the identification of over 170 microRNA families with high quantity of mapped tags (> 100 tags) (Additional file 8). MiR-30 was abundantly detected in our miRNA-Seq dataset, where it has been previously shown to be a critical regulator of kidney development . The miR-200 family was also abundantly detected in the embryonic kidney which is likely due to its role in MET regulation [47, 48]. Functional characterization of many more of kidney miRNAs identified by miRNA-Seq will be required to infer roles during organogenesis.
Mesenchymal-specific expression of miR-214/Dnm3os in the developing kidney
One of the first steps to gain insights into the biological role of miRNAs is to determine tissue localization. SISH studies based on mature miRNA sequence hybridizations can be challenging due to the limited unique sequence content of these short molecules. To overcome this, several studies have described using miRNA precursor genes, known as primary transcripts (pri-miRNA), as a proxy to monitor expression of nested miRNAs [49, 50]. Kidney miRNAs from the miRNA-Seq data were matched to corresponding intergenic noncoding pri-miRNAs (as annotated by Saini HK et al. ), that was also expressed in the mRNA-Seq data. We identified 22 highly expressed intergenic pri-miRNAs hosting kidney miRNAs including the Wilms tumor (renal neoplasm)-associated and imprinted transcript, H19,  a precursor for mir-675  and the mir-17-92 cluster Mirhg1 pri-miRNA, with the latter being involved in embryonic lung proliferation and differentiation  (Additional file 9).
Next, we identified pri-miRNAs that were represented by Affymetrix 430.2 probeset from the kidney subcompartment atlas microarray data (Additional file 10). Of these probesets, three were co-incidentally positioned to overlap the embedded miRNAs within the primary transcript (let-7b:1440357_at; miR-425:1459927_at; miR-214: 1427298_at). Of these, miR-214 from the Dnm3os host gene provided the most reliable probe set expression profile. Dnm3os has been described to serve important roles during embryo development [55, 56] although it has never been described within the context of the kidney. Micorarray probeset expression was detected in all interstitial mesenchyme subcompartments except the Six2+ nephron progenitor population (Figure 4B). SISH validation of Dnm3os/miR-214 confirmed the interstitial mesenchyme specific expression profile but was also detected in the cap mesenchyme (Figure 4B and [GUDMAP:10816]). Further validation will be required to determine which cellular population of the cap mesenchyme miR-214 is restricted to and whether it is distinct from the Six2 population.
Widespread expression of sense/anti-sense transcripts pairs in the embryonic kidney
The strand specific information of our RNA-Seq data enabled a genome-wide survey of sense-antisense transcription. Overlapping sense and antisense transcription has been described in a variety of biological roles, including RNA editing, genomic imprinting, translational regulation, RNA interference [57–60]. Current lists of validated sense-antisense pairs include many important developmental genes such as Pax2 and Hoxa11. Within the kidney, the noncoding antisense WT1 transcript (WT1-AS) shares the same expression domains as WT1 and therefore is consistent with its role as a positive regulator of WT1 protein levels . Many splice-forms of WT1-AS have been characterized, where defects in the splicing machinery are implicated with acute myeloid leukaemia . Survey of sense-antisense transcript pairs in the 15.5 dpc kidney identified 59.7% of expressed RefSeq transcripts with corresponding coding and non-coding antisense partners (Additional file 11) where only 2654 have been previously documented in the Natural Antisense Transcript Database (NATsDB) . Antisense transcripts were detected for several kidney developmental genes, including Wt1, Sall1, Pax2Lhx1, Six2, Hnf1b, Emx2 and Wnt7b, where the majority overlapped in a head-to-head orientation. Examples of tail-to-tail (Wnt9b) and embedded overlaps (Tcf21) were also detected. Only a few of these kidney development antisense transcripts (e.g Lhx1) were represented on the Affymetrix platform.
Transcriptional complexity during mesenchymal-epithelial transition
For eight loci with evidence for alternative exon usage, we scanned for changes in the protein domain composition to infer functional changes. Out of the four RefSeq canonical isoforms for Fgf8, two isoforms (variant 2 and 3) were detected in the kidney, which differed in presence or absence of exon 4 . Removal of this exon excludes the signal-peptide normally associated with this growth factor, presumably leading to an intracellular protein with a different biological role. This may have implications for the formation of the renal vesicle, the first stage of nephron induction, where Fgf8 is expressed and has assumed to act as a secreted protein.
Alternative 5' ends were identified for the Gdnf, Pax2, Eya1 and Wnt11 loci. In humans, EYA1 is associated with three isoforms differing at the first exons . In addition, RNA-Seq provided evidence for an additional uncharacterized exon between exon 1 and 2 of the canonical Eya1 RefSeq transcript EST tag evidence and gene models (Aceview: Eya1.fSep07). In the Pax2 locus, signal extending the 5' end as far as 10 kb provided compelling evidence for an alternative promoter signal beyond the current gene models.
Signal flanking 3' ends for genes such as Pax2, Bmp7, Wnt4 and Lhx1 mouse RefSeq models were supported by more complete gene models such as the human RefSeq transcripts and other gene prediction models. SISH validation of the observed Lhx1 and Wnt4 3' extensions confirms these events as an extension of the primary transcript and highlights the need for updated gene models.
Surprisingly, natural antisense transcripts were detected for 10/17 MET genes. Several antisense transcripts have previously been identified, such as Emx2os and Wt1AS where both antisense has been shown to positively regulate the respective sense transcript expression. SISH analyses of novel antisense expression for Six2, Sall1 and Lhx1 show concordant expression patterns with sense counterpart. Sense-antisense pairs identified for MET genes were arrayed in a head-to-head overlap at the 5' end which may be indicative of a bidirectional promoter, similar to Wt1-AS.
To infer candidate miRNAs involved in MET, we scanned the literature for MET genes with experimental evidence of miRNA target regulation. Only Lhx1 has been characterized as target of miR-30 within the context of kidney development . Other MET genes have had characterized miRNA regulation in other tissue types, including regulation of Hoxa11 by miR-181 during muscle differentiation , and hypoxia-induced targeting of Fgfrl1 by miR-210 . Such transcript-centric models reveal the undocumented layer of complexity associated with current models of regulatory networks which should be incorporated into functional validations studies.
Embryonic kidney development requires a high level of transcriptional co-ordination to form at least 25 known distinct cell types required to carry out specific renal functions. We have described here the first RNA-Seq profiling of whole embryonic mouse kidney and have integrated this information with previous microarray and SISH based atlases of expression during kidney development. What we show is that RNA-Seq offered detailed transcriptional profiling beyond the locus expression activity offered by most microarrays.
A major concern of whole organ profiling relates to the disproportional representation of all cell types in such complex cellular systems. Transcriptional profiling of whole organs using microarray has been problematic due to the heterogeneous tissue composition and proportions, which can overshadow differential gene expression of less abundant cell types . Given the potentially unlimited dynamic range, RNA-Seq should overcome this hurdle. We demonstrate here that at sufficient depth, whole kidney transcriptome profiling by RNA-Seq can provide the resolution and coverage to detect over 99.7% of subcompartment-specific transcripts. Transcriptional output from each major subcompartment was also shown to be evenly distributed across the data based on subcompartment-specific transcript expression, with RNA-Seq detecting both abundant (above 10 RPKM) and low-level tissue-specific transcripts (below 1 RPKM). Despite this, it is important to note that the lack of normalization approaches for RNA-Seq, makes identification of rare, cell-type specific transcripts challenging, as highly expressed transcripts would obtain the most tag coverage.
The sensitivity of RNA-Seq makes whole organ profiling ideal for integration with pre-existing microarrays of kidney cell-types to achieve single nucleotide- and spatial- resolution of transcriptional complexity. Not all events detected in the RNA-Seq could be represented by Affymetrix probesets (i.e. alternative exon and 5' promoters) due to the 3'end bias of the Affymetrix 430.2 probeset design. The 3' end bias was instead ideal for survey of differential subcompartment localization of extended 3'UTRs and detecting occasional ncRNA transcript expression.
Overall, RNA-Seq profiling captured a wide range of transcriptional complexity during kidney development. These events were highlighted among a subset of well established kidney developmental genes throughout the study revealing new insights. For example, while alternative splicing of the Wt1 locus in the kidney has been extensively documented, we detected a uncharacterized mouse in-frame isoform without exons 4 and 5. This isoform was supported by the Ensembl mouse predicted transcripts but has only been reported in fish and turtles [34–36]. These two exons together encode a putative leucine zipper motif, located at the N-terminal region of Wt1, which has been previously shown to contain protein-protein association domains . This region allows Wt1 isoforms to self-associate, whereby removal of exon 4 and 5 would alter the dimerisation of WT1 protein isoforms and their ability to interact with other proteins .
The strand-specific nature of our RNA-Seq enabled sense-antisense transcript annotations. Although various techniques confirmed widespread presence in the mammalian genome [60, 72, 73], detection and identification of low abundance antisense transcripts, a common trait of antisense RNA, remained challenging due to sequencing depth limitations from these technologies . The sequencing depth and strand-specific nature of RNA-Seq facilitated the use of a liberal approach for the identification of many sense-antisense transcripts including low-copy number antisense transcripts. In the analysis, several transcription factors critical for MET were associated with overlapping antisense ncRNA transcript expression. Many of these antisense ncRNA show syn-expression patterns with the sense pair as during SISH validation including the uncharacterized antisense for Six2, a marker of the renal progenitor cell population. The orientation is reminiscent of the Wt1 antisense (WT1AS), which has been shown to positively regulate WT1 protein expression levels  through a bidirectional promoter. Hence, this may also be true for the Six2 and Sall1 sense/antisense transcripts. Further functional validations will be required to determine antisense-mediated regulation for these key protein-coding genes.
MiRNAs have been shown to play an active role during embryonic development however individual miRNAs required for kidney development remains largely unexplored. To address this, the miRNA population from the embryonic kidney sample was isolated and sequenced to serve as a reference for the entire, embryonic kidney miRNA repertoire. Next, we associated subcompartment localization of miRNAs from intergenic pri-miRNA expression. We focused on Affymetrix probesets that directly overlapped with the embedded miRNA, which lead to the identification of miR-214 from the Dnm3os transcript. Both SISH riboprobe and Affymetrix probeset expression profiles detected expression in all kidney mesenchymal/interstitial subcompartments except cap mesenchyme, where it was detected during SISH but down-regulated in the microarray profile of the Six2+ cap mesenchyme population.
Six2 is a marker of the nephron progenitor population, which maintains progenitor renewal by preventing epithelial differentiation during MET. The inhibitory nature of miRNA, through miR-214, may reflect a role in suppressing self-renewal and therefore promoting differentiation. This hypothesis aligns with the previously described role of miR-214 as a promoter of cellular differentiation of skeletal muscle cells. miR-214 has also been shown to promote ES cell differentiation via the regulation polycomb group proteins  and by modulating Hedgehog signalling . In the kidney, Shh, part of the Hedgehog signalling pathway, is required for mesenchymal proliferation and differentiation of smooth muscle progenitor cells . This gene may also be regulated by miR-214.
Almost all the genes involved in the MET pathway show some form transcriptional complexity, which is largely unaccounted for during functional characterization of many of these loci. Hence, our findings now provide an opportunity to move towards transcript-centric models of biological pathways and networks in kidney organogenesis.
In conclusion, this dataset provides a valuable resource with which to interrogate transcriptional control of kidney development. Integration of the RNA-Seq data with pre-existing resources such as tissue-specific microarrays and SISH provides a dynamic atlas of the spatial and transcriptional regulation of a developing organ, thereby representing an ideal baseline for comparative studies into kidney development abnormalities. Specifically, our analyses highlight new transcriptional components active during key stages of kidney development that can now be prioritized for further functional characterization.
Library Prep and Sequencing of mRNA and miRNA
Total RNA (10 ug) from 46 embryonic kidney (15.5 dpc) from 5 litters of CD1 mice was put through one round of poly (A) selection (Oligotex Kit, Qiagen) followed by ribosomal depletion (Ribominus Kit, Invitrogen) to select mRNA. The enriched mRNA was fragmented by digestion with RNaseIII (Ambion), and purified on a Microcon YM30 column (Microcon). Fragmented mRNA was used to generate libraries as specified in the Whole Transcriptome Analysis Kit (Ambion) protocol for mRNA and Short RNA Expression (SREK). The SREK library was barcoded (barcode: Series A, Applied Biosystems) and pooled. Emulsions PCR (8×) and large scale enrichment (LaSE) was carried out as outlined in the SOLiD 3 Plus template bead preparation manual. Sequencing was carried out on SOLiD system 3.5 and v3.5 chemistries to produce DNA sequence reads of 35-50(nt). Datasets available via the NCBI Short Read Archive (SRA026710).
Mapping and Analysis
Mapping of SOLiD sequencing reads was performed using a recursive mapping strategy using RNA-MATE v1.1  under default settings. Reads were mapped to the mouse genome (mm9) and a library of exon-exon junctions derived from gene models such as RefSeq, UCSC known genes, Ensembl, Aceview as previously detailed in . Resulting mapped tags were presented as 'wiggle plots' (bedGraph data format) of tag abundance for visualization in UCSC Genome Browser. The mapped tag starts sites files from (the RNA-MATE output) were used to calculate tag frequency counts against RefSeq gene models.
Non-redundant RefSeq protein coding loci genomic co-ordinates was provided as BED files from the UCSC Genome Browser curation team. Tag start files were used to calculate expression as detailed in RNA-MATE manual. RefSeq gene reads per kilobases per million (RPKM) calculation was performed in Galaxy  and as detailed in .
Genome-wide identification of alternative exon and alternative 5' exon usage
A minimum of 2 tags were used to consider candidate alternate exon-exon junctions events overlapping RefSeq gene canonical junctions. As this produced a large list, we reduced the list to report only alternate exon-exon junction tags with ≥ 5 tags in Additional file 4. For alternative 5' exon usage, we used a stringent cutoff, ≥ 5 tags. This is to circumvent weaker signals in the 5' end arising from 3' bias arising from RNA-Seq protocols .
Extended 3' UTR
Tags mapping downstream of the 3'UTR boundary of RefSeq and UCSC Genes were analysed in 30 bp windows along a 20 kb (non-overlapping) radius. Presence of extended 3'UTR was calculated for genes above 1RPKM. Expression beyond the 3'UTR of RefSeq gene models were required to: a) be greater than 50% of the RPKM value b) have expression in any 10 consecutive 30 bp sliding window, and c) have expression extended greater than 500 bp.
Antisense expression were annotated against RefSeq transcripts coordinated obtained from the UCSC Genome Browser (mm9). Antisense partners were required to have expression greater than 10 RPKM. Reads were required to map on the opposite strand of the RefSeq transcript, within the annotated coding or untranslated regions.
Small RNA sequencing tags were aligned against miRBase v15 pre-miRNA hairpins using miRNA-MATE, an open source alignment tool designed in our laboratory specifically for colour-space miRNA analysis (http://grimmond.imb.uq.edu.au/miRNA-MATE/; manuscript in preparation). miRNA-MATE uses the recursive style of matching, described in Cloonan et al , for sensitive miRNA expression detection, but also can identify and strip the adaptor to determine the precise ends of the captured miRNAs. During alignment, up to 2 mismatches were allowed, treating valid-adjacent mismatches (those colour-space mismatches when located side-by side, indicate the presence of a single nucleotide variant) as a single mismatch.
Comparisons against Affymetrix probesets
Probesets were created from a consensus sequence obtained from NetAffx . The consensus sequence was mapped to the mm9 genome using blat using default parameters. Scoring of an alignment is based on UCSC Genome Browser Guidelines . If a consensus sequence matches two or more locations with the same highest score, both multi-mapping consensus sequences were included. Individual probes from each Affymetrix probeset was mapped to a library of consensus probeset sequence obtained from NetAffx. Probesets were then represented onto the genome based on the consensus sequence mapping coordinates results.
Riboprobe design and generation
The complete protocol for digoxigenin (Dig)-labeled riboprobe synthesis is available and described in detail on the GUDMAP gene expression database . Primers were ordered from Invitrogen and were designed to amplify a 3' UTR region of the RIKEN Fantom3 cDNA clone models, between 500 and 800 bp. Riboprobes were amplified from 15.5-dpc whole embryonic mouse cDNA. The 3' primers were tagged with a T7 polymerase (Roche), for in vitro transcription of Dig-labeled riboprobes. Riboprobes were then purified with lithium chloride precipitation and stored at -20°C overnight. Samples were then spun for 20 min at 4°C with supernatant discarded after the spin, gently washed with of chilled 70% ethanol, and then spun at 4°C. Supernatants were discarded and samples dried for 10 min at room temperature where pellets were then resuspended with 25 μl of water and stored at -70°C.
Section in situ hybridization validations
The complete protocol for section in-situ hybridization (SISH) is available and described in detail on the GUDMAP gene expression database . For Dnm3os, manual SISH was performed using NTM-based dye. The complete protocol is described in . Briefly 7 um paraffin sections of 15.5 dpc CD1 mouse kidneys incubated in 10 ug/ml proteinase K for 20 mins at room temperature. Next, samples were washed and refixed with 4% paraformaldehyde for 10 mins at room temperature. This is followed by acetylation and pre-hybridization using hybridization solution for 2 hrs at room temperature. Hybridization was carried out overnight at 60°C. Slides were then washed by NT buffer at room temperature before incubating for 2 h with blocking solution in a humidified chamber. A 1:1000 dilution of anti-digoxigenin antibody (Roche Applied Science) in blocking solution was added to the slides and incubated overnight at 4°C. Unbound antibodies were removed by washing in NT buffer. Sections were equilibrated in NTM buffer and incubated in color solution until purple staining was satisfactory.
Quantitative RT PCR
To validate Wt1 splice event (spliced exons 4 and 5) detected from the RNA-Seq data, the mRNA levels of the uncharacterized event was compared against a well characterized splice event (spliced exon 5) of Wt1. PCR was performed in quadruplicates using matched sample that was used to generate the RNA-Seq cDNA libraries. Samples were run with Actin housekeeping gene as a positive control. Primers were designed to span across exon junctions 3 and 6 junctions (Kidney_Wt1_minus exons 4 and 5) (Forward: CCCCTACTGACAGTTGCACA; Reverse: TACTGGGCACCACAGAGGAT). As a control, primers were also designed for a known Wt1 splice event (Kidney_Wt1_ctrl minus exon 5 (known)) (Forward: CTTGAATGCATGACCTGGAA; Reverse: TACTGGGCACCACAGAGGAT). Relative mRNA expression of Kidney_Wt1_minus exons 4 and 5 was compared to the "known" event and reported as relative mRNA fold abundance.
All animal work contributing to this manuscript was conducted according to all state, national and international guidelines. Animal ethics approval was provided by AEEC3 of The University of Queensland (Approval IMB/572/08/NIH (NF)).
We wish to thank all members from the Queensland Centre for Medical Genomics for computational resources, valuable feedback, and discussions. This work was supported by National Health and Medical Research Council grants to SMG [455857 to S.M.G. 456140, 631701] and NIH NIDDK grants to MHL and SMG (DK070136).
- Costantini F, Kopan R: Patterning a complex organ: branching morphogenesis and nephron segmentation in kidney development. Developmental cell. 2010, 18: 698-712. 10.1016/j.devcel.2010.04.008.View ArticlePubMedPubMed CentralGoogle Scholar
- Little M, Georgas K, Pennisi D, Wilkinson L, Peter K: Kidney Development: Two Tales of Tubulogenesis. Current Topics in Developmental Biology. 2010, Academic Press, 90: 193-229.Google Scholar
- Schmidt-Ott KM, Yang J, Chen X, Wang H, Paragas N, Mori K, Li JY, Lu B, Costantini F, Schiffer M, et al: Novel regulators of kidney development from the tips of the ureteric bud. Journal of the American Society of Nephrology: JASN. 2005, 16: 1993-2002. 10.1681/ASN.2004121127.View ArticlePubMedGoogle Scholar
- Challen G, Gardiner B, Caruana G, Kostoulias X, Martinez G, Crowe M, Taylor DF, Bertram J, Little M, Grimmond SM: Temporal and spatial transcriptional programs in murine kidney development. Physiological Genomics. 2005, 23: 159-171. 10.1152/physiolgenomics.00043.2005.View ArticlePubMedGoogle Scholar
- Brunskill EW, Aronow BJ, Georgas K, Rumballe B, Valerius MT, Aronow J, Kaimal V, Jegga AG, Yu J, Grimmond S, et al: Atlas of gene expression in the developing kidney at microanatomic resolution. Developmental Cell. 2008, 15: 781-791. 10.1016/j.devcel.2008.09.007.View ArticlePubMedPubMed CentralGoogle Scholar
- Stuart RO, Bush KT, Nigam SK: Changes in global gene expression patterns during development and maturation of the rat kidney. Proceedings of the National Academy of Sciences of the United States of America. 2001, 98: 5649-5654. 10.1073/pnas.091110798.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10: 57-63. 10.1038/nrg2484.View ArticlePubMedPubMed CentralGoogle Scholar
- Cloonan N, Grimmond SM: Transcriptome content and dynamics at single-nucleotide resolution. Genome biology. 2008, 9: 234-10.1186/gb-2008-9-9-234.View ArticlePubMedPubMed CentralGoogle Scholar
- Birney E, Stamatoyannopoulos JA, Dutta A, Guigo R, Gingeras TR, Margulies EH, Weng Z, Snyder M, Dermitzakis ET, Thurman RE, et al: Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature. 2007, 447: 799-816. 10.1038/nature05874.View ArticlePubMedGoogle Scholar
- Thorrez L, Tranchevent LC, Chang HJ, Moreau Y, Schuit F: Detection of novel 3' untranslated region extensions with 3' expression microarrays. BMC genomics. 2010, 11: 205-10.1186/1471-2164-11-205.View ArticlePubMedPubMed CentralGoogle Scholar
- Cloonan N, Forrest AR, Kolle G, Gardiner BB, Faulkner GJ, Brown MK, Taylor DF, Steptoe AL, Wani S, Bethel G, et al: Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nature methods. 2008, 5: 613-619. 10.1038/nmeth.1223.View ArticlePubMedGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold BCINNMJ, Pmid: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature methods. 2008, 5: 621-628. 10.1038/nmeth.1226.View ArticlePubMedGoogle Scholar
- Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y: RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome research. 2008, 18: 1509-1517. 10.1101/gr.079558.108.View ArticlePubMedPubMed CentralGoogle Scholar
- Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L: Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature biotechnology. 2010Google Scholar
- Guttman M, Garber M, Levin JZ, Donaghey J, Robinson J, Adiconis X, Fan L, Koziol MJ, Gnirke A, Nusbaum C, et al: Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nature biotechnology. 2010Google Scholar
- Tang F, Barbacioru C, Nordman E, Li B, Xu N, Bashkirov VI, Lao K, Surani MA: RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat Protocols. 2010, 5: 516-535.View ArticlePubMedGoogle Scholar
- Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, et al: mRNA-Seq whole-transcriptome analysis of a single cell. Nat Meth. 2009, 6: 377-382. 10.1038/nmeth.1315.View ArticleGoogle Scholar
- Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ: miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008, 36: D154-158. 10.1093/nar/gkn221.View ArticlePubMedGoogle Scholar
- Kobayashi A, Valerius MT, Mugford JW, Carroll TJ, Self M, Oliver G, McMahon AP: Six2 defines and regulates a multipotent self-renewing nephron progenitor population throughout mammalian kidney development. Cell Stem Cell. 2008, 3: 169-181. 10.1016/j.stem.2008.05.020.View ArticlePubMedPubMed CentralGoogle Scholar
- Self M, Lagutin OV, Bowling B, Hendrix J, Cai Y, Dressler GR, Oliver G: Six2 is required for suppression of nephrogenesis and progenitor renewal in the developing kidney. Embo J. 2006, 25: 5214-5228. 10.1038/sj.emboj.7601381.View ArticlePubMedPubMed CentralGoogle Scholar
- Li B, Ruotti V, Stewart RM, Thomson JA, Dewey CN: RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics. 2010, 26: 493-500. 10.1093/bioinformatics/btp692.View ArticlePubMedGoogle Scholar
- Wang M, Master S, Chodosh L: Computational expression deconvolution in a complex mammalian organ. BMC Bioinformatics. 2006, 7: 328-10.1186/1471-2105-7-328.View ArticlePubMedPubMed CentralGoogle Scholar
- Affymetrix - FAQ. [http://www.affymetrix.com/support/help/faqs/mouse_430/faq_8.jsp]
- Goecks J, Nekrutenko A, Taylor J, The Galaxy T: Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biology. 2010, 11: R86-10.1186/gb-2010-11-8-r86.View ArticlePubMedPubMed CentralGoogle Scholar
- Kobayashi A, Kwan K-M, Carroll TJ, McMahon AP, Mendelsohn CL, Behringer RR: Distinct and sequential tissue-specific activities of the LIM-class homeobox gene Lim1 for tubular morphogenesis during kidney development. Development. 2005, 132: 2809-2823. 10.1242/dev.01858.View ArticlePubMedGoogle Scholar
- Georgas K, Rumballe B, Valerius MT, Chiu HS, Thiagarajan RD, Lesieur E, Aronow BJ, Brunskill EW, Combes AN, Tang D, et al: Analysis of early nephron patterning reveals a role for distal RV proliferation in fusion to the ureteric tip via a cap mesenchyme-derived connecting segment. Dev Biol. 2009, 332: 273-286. 10.1016/j.ydbio.2009.05.578.View ArticlePubMedGoogle Scholar
- The Genito-Urinary Development Molecular Anatomy Project. [http://www.gudmap.org]
- Agrawal R, Tran U, Wessely O: The miR-30 miRNA family regulates Xenopus pronephros development and targets the transcription factor Xlim1/Lhx1. Development. 2009, 136: 3927-3936. 10.1242/dev.037432.View ArticlePubMedPubMed CentralGoogle Scholar
- Gilbert W: Why genes in pieces?. Nature. 1978, 271: 501-501. 10.1038/271501a0.View ArticlePubMedGoogle Scholar
- Cloonan N, Xu Q, Faulkner GJ, Taylor DF, Tang DT, Kolle G, Grimmond SM: RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data. Bioinformatics (Oxford, England). 2009, 25: 2615-2616. 10.1093/bioinformatics/btp459.View ArticleGoogle Scholar
- de Graaff E, Srinivas S, Kilkenny C, D'Agati V, Mankoo BS, Costantini F, Pachnis V: Differential activities of the RET tyrosine kinase receptor isoforms during mammalian embryogenesis. Genes & Development. 2001, 15: 2433-2444. 10.1101/gad.205001.View ArticleGoogle Scholar
- Haber DA, Sohn RL, Buckler AJ, Pelletier J, Call KM, Housman DE: Alternative splicing and genomic structure of the Wilms tumor gene WT1. Proceedings of the National Academy of Sciences of the United States of America. 1991, 88: 9618-9622. 10.1073/pnas.88.21.9618.View ArticlePubMedPubMed CentralGoogle Scholar
- Konlg A, Jakubiczka S, Wleacker P, Schlosser HW, Gessler M: Further evidence that imbalance of WT1 isoforms may be involved in Denys â€" Drash syndrome. Human Molecular Genetics. 1993, 2: 1967-1968. 10.1093/hmg/2.11.1967.View ArticleGoogle Scholar
- Spotila LD, Hall SE: Expression of a new RNA-splice isoform of WT1 in developing kidney-gonadal complexes of the turtle, Trachemys scripta. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology. 1998, 119: 761-767. 10.1016/S0305-0491(98)00053-4.View ArticleGoogle Scholar
- Klüver N, Herpin A, Braasch I, Driele J, Schartl M: Regulatory back-up circuit of medaka Wt1 co-orthologs ensures PGC maintenance. Developmental Biology. 2009, 325: 179-188. 10.1016/j.ydbio.2008.10.009.View ArticlePubMedGoogle Scholar
- Nakatsuru Y, Minami K, Yoshikawa A, Zhu J-J, Oda H, Masahito P, Okamoto N, Nakamura Y, Ishikawa T: Eel WT1 sequence and expression in spontaneous nephroblastomas in Japanese eel. Gene. 2000, 245: 245-251. 10.1016/S0378-1119(00)00016-0.View ArticlePubMedGoogle Scholar
- Beltcheva O, Kontusaari S, Fetissov S, Putaala H, Kilpelainen P, Hokfelt T, Tryggvason K: Alternatively Used Promoters and Distinct Elements Direct Tissue-Specific Expression of Nephrin. J Am Soc Nephrol. 2003, 14: 352-358. 10.1097/01.ASN.0000043081.65110.C4.View ArticlePubMedGoogle Scholar
- Ruotsalainen V, Ljungberg Pi, Wartiovaara J, Lenkkeri U, KestilÃ¤ M, Jalanko H, Holmberg C, Tryggvason K: Nephrin is specifically located at the slit diaphragm of glomerular podocytes. Proceedings of the National Academy of Sciences of the United States of America. 1999, 96: 7962-7967. 10.1073/pnas.96.14.7962.View ArticlePubMedPubMed CentralGoogle Scholar
- Nishinakamura R, Matsumoto Y, Nakao K, Nakamura K, Sato A, Copeland NG, Gilbert DJ, Jenkins NA, Scully S, Lacey DL, et al: Murine homolog of SALL1 is essential for ureteric bud invasion in kidney development. Development (Cambridge, England). 2001, 128: 3105-3115.Google Scholar
- Rothenpieler UW, Dressler GR: Pax-2 is required for mesenchyme-to-epithelium conversion during kidney development. Development (Cambridge, England). 1993, 119: 711-720.Google Scholar
- Patel SR, Dressler GR: Expression of Pax2 in the intermediate mesoderm is regulated by YY1. Developmental Biology. 2004, 267: 505-516. 10.1016/j.ydbio.2003.11.002.View ArticlePubMedGoogle Scholar
- Kloosterman WP, Plasterk RHA: The Diverse Functions of MicroRNAs in Animal Development and Disease. Developmental cell. 2006, 11: 441-450. 10.1016/j.devcel.2006.09.009.View ArticlePubMedGoogle Scholar
- Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116: 281-297. 10.1016/S0092-8674(04)00045-5.View ArticlePubMedGoogle Scholar
- Harvey SJ, Jarad G, Cunningham J, Goldberg S, Schermer B, Harfe BD, McManus MT, Benzing T, Miner JHCINJASNN, Pmid: Podocyte-specific deletion of dicer alters cytoskeletal dynamics and causes glomerular disease. Journal of the American Society of Nephrology: JASN. 2008, 19: 2150-2158. 10.1681/ASN.2008020233.View ArticlePubMedPubMed CentralGoogle Scholar
- Wei Q, Bhatt K, He HZ, Mi QS, Haase VH, Dong Z: Targeted deletion of Dicer from proximal tubules protects against renal ischemia-reperfusion injury. Journal of the American Society of Nephrology: JASN. 2010, 21: 756-761. 10.1681/ASN.2009070718.View ArticlePubMedPubMed CentralGoogle Scholar
- Nagalakshmi VK, Ren Q, Pugh MM, Valerius MT, McMahon AP, Yu J: Dicer regulates the development of nephrogenic and ureteric compartments in the mammalian kidney. Kidney Int. 2011, 79: 317-330. 10.1038/ki.2010.385.View ArticlePubMedGoogle Scholar
- Park S-M, Gaur AB, Lengyel E, Peter ME: The miR-200 family determines the epithelial phenotype of cancer cells by targeting the E-cadherin repressors ZEB1 and ZEB2. Genes & Development. 2008, 22: 894-907. 10.1101/gad.1640608.View ArticleGoogle Scholar
- Gregory PA, Bert AG, Paterson EL, Barry SC, Tsykin A, Farshid G, Vadas MA, Khew-Goodall Y, Goodall GJCINNCBM, Pmid: The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1. Nature cell biology. 2008, 10: 593-601. 10.1038/ncb1722.View ArticlePubMedGoogle Scholar
- Gennarino VA, Sardiello M, Avellino R, Meola N, Maselli V, Anand S, Cutillo L, Ballabio A, Banfi S: MicroRNA target prediction by expression analysis of host genes. Genome Research. 2009, 19: 481-490.View ArticlePubMedPubMed CentralGoogle Scholar
- Baskerville S, Bartel DP: Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes. RNA. 2005, 11: 241-247. 10.1261/rna.7240905.View ArticlePubMedPubMed CentralGoogle Scholar
- Saini HK, Enright AJ, Griffiths-Jones S: Annotation of mammalian primary microRNAs. BMC genomics. 2008, 9: 564-10.1186/1471-2164-9-564.View ArticlePubMedPubMed CentralGoogle Scholar
- Moulton T, Crenshaw T, Hao Y, Moosikasuwan J, Lin N, Dembitzer F, Hensle T, Weiss L, McMorrow L, Loew T, et al: Epigenetic lesions at the H19 locus in Wilms' tumour patients. Nat Genet. 1994, 7: 440-447. 10.1038/ng0794-440.View ArticlePubMedGoogle Scholar
- Cai X, Cullen BR: The imprinted H19 noncoding RNA is a primary microRNA precursor. RNA. 2007Google Scholar
- Lu Y, Thomson JM, Wong HYF, Hammond SM, Hogan BLM: Transgenic over-expression of the microRNA miR-17-92 cluster promotes proliferation and inhibits differentiation of lung epithelial progenitor cells. Developmental Biology. 2007, 310: 442-453. 10.1016/j.ydbio.2007.08.007.View ArticlePubMedPubMed CentralGoogle Scholar
- Watanabe T, Sato T, Amano T, Kawamura Y, Kawamura N, Kawaguchi H, Yamashita N, Kurihara H, Nakaoka T: Dnm3os, a non-coding RNA, is required for normal growth and skeletal development in mice. Developmental dynamics: an official publication of the American Association of Anatomists. 2008, 237: 3738-3748.View ArticleGoogle Scholar
- Loebel DA, Tsoi B, Wong N, Tam PP: A conserved noncoding intronic transcript at the mouse Dnm3 locus. Genomics. 2005, 85: 782-789. 10.1016/j.ygeno.2005.02.001.View ArticlePubMedGoogle Scholar
- Faghihi MA, Wahlestedt C: Regulatory roles of natural antisense transcripts. Nature reviews Molecular cell biology. 2009, 10: 637-643. 10.1038/nrm2738.View ArticlePubMedPubMed CentralGoogle Scholar
- Shendure J, Church GM: Computational discovery of sense-antisense transcription in the human and mouse genomes. Genome biology. 2002, 3: RESEARCH0044-View ArticlePubMedPubMed CentralGoogle Scholar
- Faghihi MA, Zhang M, Huang J, Modarresi F, Van der Brug MP, Nalls MA, Cookson MR, St-Laurent G, Wahlestedt C: Evidence for natural antisense transcript-mediated inhibition of microRNA function. Genome biology. 2010, 11: R56-10.1186/gb-2010-11-5-r56.View ArticlePubMedPubMed CentralGoogle Scholar
- Katayama S, Tomaru Y, Kasukawa T, Waki K, Nakanishi M, Nakamura M, Nishida H, Yap CC, Suzuki M, Kawai J, et al: Antisense transcription in the mammalian transcriptome. Science (New York, NY). 2005, 309: 1564-1566.View ArticleGoogle Scholar
- Potter SS, Branford WW: Evolutionary conservation and tissue-specific processing of Hoxa 11 antisense transcripts. Mammalian Genome. 1998, 9: 799-806. 10.1007/s003359900870.View ArticlePubMedGoogle Scholar
- Moorwood K, Charles AK, Salpekar A, Wallace JI, Brown KW, Malik K: Antisense WT1 transcription parallels sense mRNA and protein expression in fetal kidney and can elevate protein levels in vitro. The Journal of pathology. 1998, 185: 352-359. 10.1002/(SICI)1096-9896(199808)185:4<352::AID-PATH119>3.0.CO;2-#.View ArticlePubMedGoogle Scholar
- Dallosso AR, Hancock AL, Malik S, Salpekar A, King-Underwood L, Pritchard-Jones K, Peters J, Moorwood K, Ward A, Malik KTA, Brown KW: Alternately spliced WT1 antisense transcripts interact with WT1 sense RNA and show epigenetic and splicing defects in cancer. RNA. 2007, 13: 2287-2299. 10.1261/rna.562907.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang Y, Li J, Kong L, Gao G, Liu Q-R, Wei L: NATsDB: Natural Antisense Transcripts DataBase. Nucleic Acids Research. 2007, 35: D156-D161. 10.1093/nar/gkl782.View ArticlePubMedGoogle Scholar
- Alfano G, Vitiello C, Caccioppoli C, Caramico T, Carola A, Szego MJ, McInnes RR, Auricchio A, Banfi S: Natural antisense transcripts associated with genes involved in eye development. Human Molecular Genetics. 2005, 14: 913-923. 10.1093/hmg/ddi084.View ArticlePubMedGoogle Scholar
- Noonan FC, Goodfellow PJ, Staloch LJ, Mutch DG, Simon TC: Antisense transcripts at the EMX2 locus in human and mouse. Genomics. 2003, 81: 58-66. 10.1016/S0888-7543(02)00023-X.View ArticlePubMedGoogle Scholar
- MacArthur CA, Lawshe A, Xu J, Santos-Ocampo S, Heikinheimo M, Chellaiah AT, Ornitz DM: FGF-8 isoforms activate receptor splice forms that are expressed in mesenchymal regions of mouse development. Development. 1995, 121: 3603-3613.PubMedGoogle Scholar
- Abdelhak S, Kalatzis V, Heilig R, Compain S, Samson D, Vincent C, Levi-Acobas F, Cruaud C, Le Merrer M, Mathieu M, et al: Clustering of mutations responsible for branchio-oto-renal (BOR) syndrome in the eyes absent homologous region (eyaHR) of EYA1. Human molecular genetics. 1997, 6: 2247-2255. 10.1093/hmg/6.13.2247.View ArticlePubMedGoogle Scholar
- Naguibneva I, Ameyar-Zazoua M, Polesskaya A, Ait-Si-Ali S, Groisman R, Souidi M, Cuvellier S, Harel-Bellan A: The microRNA miR-181 targets the homeobox protein Hox-A11 during mammalian myoblast differentiation. Nat Cell Biol. 2006, 8: 278-284. 10.1038/ncb1373.View ArticlePubMedGoogle Scholar
- Mathew LK, Simon MC: mir-210: A Sensor for Hypoxic Stress during Tumorigenesis. Molecular cell. 2009, 35: 737-738. 10.1016/j.molcel.2009.09.008.View ArticlePubMedPubMed CentralGoogle Scholar
- Holmes G, Boterashvili S, English M, Wainwright B, Licht J, Little M: Two N-Terminal Self-Association Domains Are Required for the Dominant Negative Transcriptional Activity of WT1 Denys-Drash Mutant Proteins. Biochemical and Biophysical Research Communications. 1997, 233: 723-728. 10.1006/bbrc.1997.6545.View ArticlePubMedGoogle Scholar
- He Y, Vogelstein B, Velculescu VE, Papadopoulos N, Kinzler KW: The Antisense Transcriptomes of Human Cells. Science. 2008, 322: 1855-1857. 10.1126/science.1163853.View ArticlePubMedPubMed CentralGoogle Scholar
- Ge X, Wu Q, Jung Y-C, Chen J, Wang SM: A large quantity of novel human antisense transcripts detected by LongSAGE. Bioinformatics. 2006, 22: 2475-2479. 10.1093/bioinformatics/btl429.View ArticlePubMedGoogle Scholar
- Faghihi MA, Wahlestedt C: Regulatory roles of natural antisense transcripts. Nat Rev Mol Cell Biol. 2009, 10: 637-643. 10.1038/nrm2738.View ArticlePubMedPubMed CentralGoogle Scholar
- Juan AH, Kumar RM, Marx JG, Young RA, Sartorelli V: Mir-214-dependent regulation of the polycomb protein Ezh2 in skeletal muscle and embryonic stem cells. Molecular cell. 2009, 36: 61-74. 10.1016/j.molcel.2009.08.008.View ArticlePubMedPubMed CentralGoogle Scholar
- Flynt AS, Li N, Thatcher EJ, Solnica-Krezel L, Patton JGCINNGF, Pmid: Zebrafish miR-214 modulates Hedgehog signaling to specify muscle cell fate. Nature genetics. 2007, 39: 259-263. 10.1038/ng1953.View ArticlePubMedPubMed CentralGoogle Scholar
- Yu J, Carroll TJ, McMahon AP: Sonic hedgehog regulates proliferation and differentiation of mesenchymal cells in the mouse metanephric kidney. Development. 2002, 129: 5301-5312.PubMedGoogle Scholar
- NetAffx (Affymetrix Analysis Center). [http://www.affymetrix.com/analysis/index.affx]
- UCSC Genome Browser Guidelines. [http://hgwdev.cse.ucsc.edu/FAQ/FAQblat#blat4]
- GUDMAP Experimental Protocols. [http://www.gudmap.org/Research/Protocols/Little.html]
- Wilhelm D, Hiramatsu R, Mizusaki H, Widjaja L, Combes AN, Kanai Y, Koopman P: SOX9 Regulates Prostaglandin D Synthase Gene Transcription in Vivo to Ensure Testis Development. Journal of Biological Chemistry. 2007, 282: 10553-10560. 10.1074/jbc.M609578200.View ArticlePubMedGoogle Scholar
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