Introduction

Approximately 1 in 3 deaths in industrialized countries are caused by cancer, with the majority of deaths arising from solid tumors [1]. The most prevalent solid cancers account for almost half of all cancers in highly developed countries [2]. It has become clear that effective therapy must address tumor cell heterogeneity and the microenvironment [3]. Intratumoral areas contain high physiological variability in nutrients, pH, and oxygen availability leading to tumor cell heterogeneity [4, 5]. Low oxygen availability (hypoxia) is particularly deleterious to patient survival as it renders tumor cells more resistant to chemotherapy, radiotherapy, and immunotherapy [5]. Resistance to treatment can be due to both the characteristics of the hypoxic microenvironment and intrinsic cancer cell features [5]. To survive hypoxic conditions, tumor cells adapt through the Hypoxia Induced Factors (HIFs), which promote phenotypes including but not limited to cell survival, motility, angiogenesis, and altered glucose metabolism. As a result, hypoxia adaptation is regarded as a fundamental force driving tumor cell pathogenesis [5]. Therefore, an accurate understanding of the breadth of hypoxic adaptations and consequences is essential to the development of more effective therapeutics.

Although hypoxia adaptation is primarily orchestrated by HIF1α, HIF2α has also been shown to play an important role [5]. Both proteins are regulated through oxygen-dependent pathways triggered by proline hydroxylation leading to proteasomal degradation under normal oxygen conditions (normoxia) [5]. Under hypoxic conditions (<5% O2), both HIF1α and HIF2α escape degradation, translocate to the nucleus, and associate with HIF1β/ARNT to form the functional transcription factors HIF1 and HIF2, respectively, and initiate transcription [5]. HIF1 drives the transcription of hundreds of mRNAs and miRNAs that enable cell adaptation to and beyond hypoxia such as genes linked to metastasis through the induction of epithelial to mesenchymal transition (EMT), which can occur through canonical and non-canonical pathways [2A). After verifying the expression of all components of the miRNA biogenesis and effector pathways during LTHY (Fig. S2B), we performed hierarchical clustering analyses, which revealed a dynamic DEmiR landscape across conditions with two clusters (clusters 3&4) being highly regulated at the 1-0.5% and 0.5-0.1% O2 transitions. (Fig. 2A). As a validation step, we examined miR-210-3p, a canonical hypoxia-induced miRNA. Although miR-210-3p was already elevated at 5% O2, indicative of some level of hypoxic stress, levels were further significantly upregulated during LTHY, indicative of an increased state of hypoxic stress (Fig. 2A, B). These results agree with our previous observations made with HIF1α-GFP (Fig. 1D).

Fig. 2: Long-term hypoxia adaptation induces an miRNA signature linked to EMT.
figure 2

A Hierarchically clustered heatmap of all differentially expressed (Benjamini-Hochberg adjusted p-value < 0.05 in the 5% vs 0.1% O2 comparison, and > 100 normalized DESeq2 reads in any condition) miRs, normalized to contribution to total expression in the dataset. Red denotes the classical hypoxia-induced miR, miR-210-3p. Green denotes EMT-promoting miRs miR-221/222. Blue and purple denote other miRs of interest. Heatmap clustered using WardD.2 hierarchical linkage metric, with the number of clusters chosen subjectively. B Expression values for miR-210-3p. C Expression values for miR-125b-1-3p. D Expression values for miRs of interest in cluster 4. E Expression values for miRs of interest in cluster 5. F, G Expression values for genes Cpeb1 and Sema6d. B, G Expression levels are DESeq2 normalized reads. * denotes relative significance as calculated by DESeq2 Benjamini-Hochberg adjusted p-value (padj). *padj < 0.05, **padj < 0.01, ***padj < 0.001.

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Interestingly the top DEmiR at 0.1% O2 was miR-125b-1-3p, which has been linked to increased metastatic potential in colorectal cancer cells and was the top upregulated miR in an EMT-inducing assay using pancreatic cancer cells along with miR-100-5p (Fig. 2A, C) [20, 21]. All other miRs present in this cluster also have links to EMT and have been shown to be regulators of TGFβ-induced EMT [22,23,24,25]. Remarkably, except for the uncharacterized miR-3965, all of the miRs that were significantly upregulated at 0.5% O2, concomitant with observed morphological changes, are known to be positively correlated or directly involved in EMT (Fig. 2D) [26,27,28,29,30,31,32,33,34,35]. Most notably, miR-221 and miRs-222 are directly involved in EMT [26, 27, 36,4A). We confirmed that unexpressed exons were present and without mutation in the B16-HG genome, confirming the genomic integrity of the locus (Fig. S4B and supporting material). RNAseq read coverage began 195 bp upstream of exon 6, within intron 5, suggesting that transcription was being initiated from a previously undescribed transcription start site (TSS). To investigate a potential promoter region upstream of the RNAseq read coverage, we performed a Transcription Factor Binding Site (TFBS) analysis across the entire 20 kb intron 5 sequence, considering only the transcription factors expressed at 0.5% O2 (Fig. 4B). With this approach, we identified several HIF1 binding sites (HREs) within intron 5 and determined that all these HREs were accessible to Hif1 by ChIP-qPCR (Fig. 4B, Fig. S4C). In addition, several other TFBSs for transcription factors expressed in the B16-HG cell line at 0.5% O2 were identified throughout the intron, suggesting extensive transcriptional regulation within intron 5 (Fig. S4D).

Fig. 4: The long-term hypoxia adaptation induces novel truncated Wt1 mRNA transcripts from an intronic HRE-driven promoter.
figure 4

A LTHY read coverage of the Wt1 locus, at 0.1% O2 of the LTHY time course, generated in IGV. Number ranges are coverage depths at the nucleotide level. Histogram is representative of replicates (n = 2, n2 shown). Introns 1–4 are condensed for visual clarity. B Transcription Factor Binding Site analysis of murine Wt1 intron 5 from beginning of intron 5 to beginning of RNAseq read coverage for Wt1. Only considered TFBSs with a score ≥ 0.95. Intron 5 sequence is broken into 40 bins, ~500 bp/bin. Analysis done using TFBStools in R. C FACS samples of the empty promoter-reporter construct, and the wild-type (WT) across the LTHY time course. Gate represents mCherry+ gate used for promoter activity calculations. FACS plots are representative of their triplicates. D Functional investigation into tWT1 promoter subregions. “Dist”: Distal region. “P1”: Proximal subregion 1. “P2”: Proximal subregion 2. “pT”: Poly-Thymine stretch. +: DNA region is present. −: DNA region is not present. M: DNA region has specific TFBSs scrambled. Promoter activity calculated using a ratio ZsGreen expression in transduced cells relative to untransduced cells, normalized to their normoxic counterparts. Significance calculated using 2-way ANOVA with Tukey’s multiple comparisons. Black stars represent intra-construct statistical comparisons; only reporting statistics relative to 5% O2. Blue stars represent significance relative to WT at 0.1% O2. Other comparisons are not shown for visual clarity. E Functional investigation into the P1 subregion of the tWt1 promoter at 0.1% O2. Promoter activity was calculated as in D. Statistics are a 2-way ANOVA with Tukey’s multiple comparisons test. Black stars represent statistical comparisons. Blue stars represent significance relative to WT at 0.1% O2. Other comparisons not shown for visual clarity. D, E “-“: an absence of subregion. “+“: presence of wild-type sequence. “M”: Transcription factor binding sites listed in Fig. S4F are scrambled. *p < 0.05. **p < 0.01. ***p < 0.001 ****p < 0.0001.

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Given the hypoxia-dependent nature of Wt1 upregulation and the binding of Hif1 to intron 5 HREs, we investigated whether the genomic region upstream of the RNAseq coverage constituted a functional promoter. To do so, we developed a reporter construct, which constitutively expresses mCherry and where ZsGreen expression is driven by the putative promoter or variants thereof (Fig. S4E). The putative promoter encompassing 551 bp upstream of the TSS, was broken down into four distinct regions (Fig. S4F). Upstream from the TSS, the first region is the poly-thymine (PolyT) stretch due to its sequence composition. Beyond this is the proximal region, which was subdivided into P1 and P2, and the distal region, which contains a long poly-AG stretch. It is important to note that we observed no changes in the expression of all the transcription factors associated with the TFBSs in these regions, apart from Nr4a2 downregulation (Fig. S4G).

To gain insights into the transcriptional regulatory ability of each subregion and TFBS, we built a panel of promoters consisting of either subregion deletions or TFBS mutation. We then performed a transcriptional activity screen in B16 cells and monitored mCherry and ZsGreen expression across LTHY. As control, the cells were cultured in parallel in normoxic conditions. As expected, the “empty” version of the promoter-reporter system did not respond to LTHY (Fig. 4C, D). Contrastingly, the “wild-type” putative promoter induced ZsGreen in a pattern that mimicked the kinetics of Wt1 during LTHY, demonstrating its role as a hypoxia-sensitive promoter (Fig. 4C, D). Conversely, when all the P1 TFBSs were mutated, we observed a significant and substantial reduction in ZsGreen levels, suggesting its role as the main driver of LTHY-induced Wt1 expression. Intriguingly, when the P2 TFBSs were mutated, expression levels of ZsGreen significantly increased, suggesting a role as a negative regulator of transcription (Fig. 4D). The distal region also appears to possess some transcriptional activity, as there was a small but significant increase in ZsGreen levels when it was the only constituent of the putative promoter.

Finally, we mutated each HRE within P1 to assess their individual role in regulating tWt1 expression during LTHY. Our data indicates that while both HREs contribute to the promoter activity, HRE #2 seems to possess greater transcriptional activity as a standalone element (Fig. 4E). Mutation of the RUNX1 and NFATC2 sites minimally altered ZsGreen expression in the context of HRE-deficient conditions indicating they were non-functional in this context. Together, our data establishes the genomic region within intron 5 of murine WT1 as a bona fide hypoxia-sensitive promoter through necessary and sufficient HIF1 binding sites, can initiate transcription of Wt1 at 0.5% O2, and increase transcriptional activity as cells adapt to more severe hypoxia.

Identification and characterization of truncated Wt1 transcripts

Next, we investigated the functionality of the novel truncated Wt1 (tWt1) transcripts as RNAseq coverage analyses revealed the presence of exonic spikes and read junctions suggesting a mature mRNA. These analyses also revealed a novel splicing event joining the 3’ end of intron 5 to the 5’ end of exon 7 leading to a novel RNA which excludes exon 6 (Fig. 5A). Canonical exon 6 to exon 7 splicing was also observed in some transcripts but constituted the minority of splicing events. Our analyses also identified the known KTS splicing event, which introduces a lysine-threonine-serine motif between zinc fingers 3 and 4 of WT1 between exons 9-10, at a near 1:1 frequency, in line with previous reports [14, 61]. The novel splicing site within intron 5 occurred 58nt upstream of exon 6 (Fig. S5A). Interestingly, when either splicing event occurs, it adds an intronic sequence to the beginning of the tWt1 mRNA transcripts upstream of exon 6 or exon 7, and introduces potential start codons (Fig. 5B, Fig. S5A). Based on the observed splicing events, there are four possible mRNA species, named for their first canonical exon (E6, E7), and the presence of the KTS motif (E6K, E7K) (Fig. 5C).

Fig. 5: Novel truncated Wt1 transcripts encode efficiently translated proteins that accumulate in the nucleus.
figure 5

A Splicing events observed in LTHY data. Percentages are the average between replicates, coverage depths are overlaid. B Potential open reading frames (ORFs) derived from the tWt1 intron 5 sequence in E7 isoforms. Purple: Intron 5 derived sequence. Orange: Exon 7 derived sequence. Bold: Canonical WT1 ORF (third ORF). Bright-green/dark-green: In/out of frame start codons. Red: Stop codons. C Possible tWt1 isoforms. Purple: Intronic sequence. Orange: Exonic sequence. Blue: KTS motif. D GFP levels of DOX-induced expression of tWT1-GFP isoforms in B16 cells. E Microscopy images of tWT1-GFP fusion constructs. Nuclear staining was performed using Hoechst 33342 (Thermo FIsher: H1399) as per the manufacturers protocol. F Western Blot of HEK cells expressing DOX inducible GFP or E7K-tWT1-GFP. Top: anti-Calnexin. Bottom: anti-GFP. G Mass Spectrometry (MS) coverage of E7K-tWt1 purified from HEK cells. Refer to the legend for full annotation.

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To determine whether any of these tWt1 transcripts could be translated to produce functional protein, we fused each of them to a C-terminal, ATG-deficient, GFP in doxycycline-inducible lentiviral vectors (Fig. S5B). This ensures fluorescence only occurs via an in-frame functional start codon within the tWt1 transcript (Fig. S5C). Cell lines stably expressing the various tWt1 transcripts were treated with doxycycline and analyzed by FACS to determine the level of tWt1-GFP expression, while subcellular localization was determined by confocal microscopy. (Fig. 5D, E). Following Dox induction, both the E7 and E7K variants produced a robust GFP signal and were localized to the nucleus, as expected based on WT1, with E7K displaying clear nucleolar accumulation, a known attribute of KTS + WT1 isoforms [62]. In contrast, E6K-GFP failed to generate substantial GFP expression or nuclear localization, suggesting non-functionality for both E6 isoforms.

Western blot analysis of the E7K -GFP fusion protein, which showed a substantial band at 48kDA, suggested translation initiation within the intron 5 derived sequence, which was confirmed by mass spectrometry (MS) analyses of immunoprecipitated E7K-GFP (Fig. 5F, G, Fig. S5E). Interestingly, translation initiation of the E7 polypeptide correlated with the Kozak context of the in-frame start codons, with the strongest Kozak signal at the second intron 5 derived in-frame start codon (Fig. S5D). Kozak strength of the in-frame ATGs also explains lack of E6-GFP translation, as the putative intron 5 derived ATG is out of frame with tWt1 in the E6 variant, and no other strong in-frame ATGs are present in E6 (Fig. S5D).

LTHY-induced tWt1 retains DNA-binding and links to EMT

Due to the unambiguous nuclear localization of E7-GFP, we sought to validate its functionality. To do so, we performed ChIPSeq with anti-GFP on E7- eGFP B16 cells after 36 h at 0.5% O2 as per the LTHY protocol. As controls, we used both input ChIP DNA, and a critically truncated version of Wt1 (cWt1) which loses nuclear localization and therefore does not bind to DNA (Fig. 6A). Our ChIPseq analyses identified 865 genes (Table 1). Motif analysis showed significant enrichment for the known Wt1 motif, which was found in 36% of peaks, and a de novo motif in 30% of peaks, which only differed in some preferred nucleotides (Fig. 6B). Regardless of whether or not they contained the WT1 binding motifs, peaks were predominantly found near the TSS, suggesting that E7-tWt1 acts as a promoter, similar to WT1 (Fig. S6A) [63]. Functional annotation analyses revealed significant enrichment for transcription and cell adhesion annotation clusters, with a specific enrichment of cell-cell adhesion annotations (Fig. 6C, D, Fig. S6B).

Fig. 6: Novel E7-tWT1 isoform retains DNA binding ability and is associated with genes involved in EMT.
figure 6

A Left: top: schematic of cWt1 CDS. GFP was linked C-terminally as per the E7-GFP construct. Bottom: microscopy image of cWt1 under Dox induction. Right: GFP induction levels of cWt1 relative to E7-GFP. Induction was performed after 36 h of incubation at 0.5% O2, as per the LTHY protocol. All Dox inductions were performed at 2 µg/mL. B Known and de novo TF motif analysis of E7-K tWT1 ChIPseq data. The known motif p-value = 1e−78, is found in 36% of called peaks. The de novo motif p-value = 1e−105, motif is found in 30% of ChIPseq peaks. C, D Functional annotation bubbleplots of ChIPseq called peaks. E E7-tWT1 ChIPseq coverage and LTHY RNAseq expression profiles for genes of interest. cWt1 and input chromatin were used as negative controls. Black arrow denotes CDS start. * denotes relative significance as calculated by DESeq2 Benjamini-Hochberg adjusted p-value (padj). *padj < 0.05, **padj < 0.01, *** padj < 0.001.

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Table 1 Breakdown of ChIPSeq genomic locations.
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We also identified several genes associated with EMT, which had expression kinetics matching those of E7-Wt1 and the appearance of EMT-like features (Fig. 6E). Indeed, Zyx, Lpp, and Vasn are known cellular motility genes, and Gadd45g, Cxxc5, and Smad7 can influence EMT through transcriptional regulation. Cxxc5 is a known WT1 (-KTS) target gene, providing additional strength to the validity of the dataset, functionality of E7-tWt1 and its potential role in mediating LTHY-induced EMT [64].

Identification of tWT1 in human cancers and prognostic value

Finally, we moved towards determining whether induction of tWt1 in cancer cells undergoing long-term and severe hypoxia adaptation could be observed in human cancers and whether we could infer a prognostic value to its expression considering its link to EMT. To do so, we performed qPCR analyses on human melanoma and breast cancer cell lines undergoing LTHY adaptation using primer pairs that enable us to determine the expression of canonical WT1 or tWT1. Our results indicate that most tumor cells tested significantly induced the expression of tWT1 following LTHY adaptation, except for the MEL537 melanoma cell line which constitutively expressed tWT1 (Fig. 7A, B, Fig. S7A, B). This points to a generalized mechanism of expression regulation across species and cancer types. Interestinlgy, the breast cancer cell line tested displayed significant increase in tWT1 induction during LTHY in the presence of the ERα agonist estradiol (E2) (Fig. S7B).

Fig. 7: Novel tWT1 transcripts are expressed in human cancers and are indicative of poor long-term survival in ovarian cancers.
figure 7

A tWT1 expression in human melanoma cell lines undergoing LTHY as measured by qPCR. Expression presented as relative to fold change (FC) to the 5% O2 condition B tWT1 expression in ZR75 cells during LTHY incubation in the absence of estradiol (E2), relative to the 5% O2 timepoint. C Left: sunburst plot of Leucegene samples based on WT1 gene and isoform expression. Iso WT1 denotes the number of samples where WT1 isoform calling could be performed. Right: Breakdown of tWT1-G/P expression levels in tWT1 expressing Leucegene samples. Isoform calling was done using km and the isoform specific difference between G and P, using km’s Expectation-Maximization algorithm. D tWT1-GFP expression levels as determined by FACS in HEK293T cells. tWT1-GFP expression was induced by 2 µg/mL DOX for 36 hs at the 0.5% O2 timepoint of the LTHY protocol. E Left: Sunburst plot of TCGA-OV samples by WT1 isoform expression. Right: Breakdown of tWT1-G/P expression levels in tWT1-G/P expressing TCGA-OV samples. Isoform calling was done using km and the isoform-specific difference between G and P, using km’s Expectation-Maximization algorithm. F WT1 and tWT1 mRNA expression in human ovarian cancer cell line OVCAR3 after completing the LTHY adaptation, relative to normoxia. G RT-PCR of human tWT1 isoforms from normoxic OVCAR3 cDNA. H Delta-CT values calculated from OVCAR3 and TOV3291G cells in normoxic conditions using RPL10 as housekee** gene to assess baseline expression. I WT1 and tWT1 mRNA expression in human ovarian cancer cell line TOV3291G after completing LTHY, relative to normoxia. Data presented as relative expression to normoxic condition, J Survival curve analyses for TCGA-OV (ovarian cancer) based on WT1 expression subsets. Left: Kaplan-Meier estimation survival curve of TCGA-OV samples, comparing samples which express any isoform of WT1 versus those with no WT1 expression. Curves are not significantly different (p = 0.26). Right: Kaplan-Meier estimation survival curve of TCGA-OV samples, comparing samples which express tWT1 isoforms (isoforms G or P) versus those which exclusively express canonical WT1 isoforms. Two-sided p-value of the whole curve is 0.12. Two-sided p-value of post-median data (126 observations) is 0.039. WT1 expression and isoform calling were determined by detection of exons 1, 1a, 2, 4, 7, and isoform G exon 1 by km.

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Next, we investigated whether LTHY-induction of tWT1 mRNA transcripts in humans was similar to that observed in mice. The genomic landscape of the WT1 locus is similar between humans and mice, suggesting potential similarities in intragenic regulation of transcription (Fig. S7C, top). We first analyzed RNAseq data from AML-patient data from the Leucegene database due to its known prevalence of WT1 expression and high depth of sequencing, which facilitates the identification of splice variants using an alignment-free Kmer approach [65, 66]. This enabled us to identify a previously characterized WT1 isoform, annotated as G (G-tWT1), and a new isoform we termed P (P-tWT1) (Fig. S7C). Both isoforms arise from an intron 5 TSS, where G-tWT1 has a splicing event within intron 5 and exon 6, while P-tWT1 displays a continuous sequence from intron 5 into exon 6 (Fig. S7C) [67]. A breakdown of the Leucegene dataset revealed that most patient samples express WT1 at the RNA level, with 77 samples exclusively expressing either tWT1 isoforms. Of those 77 samples, 37 exclusively expressed P-tWT1. (Fig. 7C, left). When plotted against each other, we observed a bias towards the expression of P-tWT1 over G-tWT1 (Fig. 7C, right). While the added intronic sequence in G-tWT1 does not introduce an in-frame ATG like E7-tWt1, Dechsukhum and colleagues previously reported that translation initiated through a non-canonical CUG start codon found in the added intronic sequence (Fig. S7D) [67]. In contrast, the inclusion of the elongated intronic sequence in P-tWT1 introduces an in-frame ATG with similar Kozak strength to the functional ATG in the murine E7-tWt1, suggesting that it could be translated in a similar fashion (Fig. S7D, E). Combined with the expression bias in tumor samples, P-tWT1 appears to be the more relevant isoform. We validated this by fusing the G- and P-tWT1 RNA sequence to ATG-deficient GFP under the control of a Dox-inducible promoter as done previously for murine tWt1. The new constructs (G-tWT1 and P-tWT1) were transduced into HEK293T cells, and we monitored GFP expression by flow cytometry and characterized the protein expression profiles by Western blot (Fig. 7D, Fig. S7F). Not only was P-tWT1 more highly expressed than G-tWT1, but translation resulted in a fusion protein similar in size, as expected from the RNAseq, as that of the functionally active murine E7-tWt1 and detected with both the GFP and a C-terminally conserved epitope within exon 7 (Fig. S7F).

We next investigated the expression of tWT1 isoforms across cancer patient samples and determined their value as prognostic markers through The Cancer Genome Atlas (TCGA) database. Surprisingly, within TCGA, tWT1 isoforms were exclusively identified in ovarian cancer (TCGA-OV), which is also the subset with the highest WT1 expression [68]. In contrast to the Leucegene dataset, no samples could be identified with exclusive tWT1 expression, as canonical WT1 expression was always concomitant (Fig. 7E, left). Additionally, the tWT1 isoform expression bias towards P-tWT1 was much more pronounced in ovarian cancer when compared to Leucegene (Fig. 7E, right). As ovarian cancers are known for being highly hypoxic, we tested whether ovarian cancer cell lines adapting to LTHY would also display increases in tWT1 expression and undergo EMT. For this, we first tested the well-characterized cancer line OVCAR3. There was no significant change in WT1 and tWT1 mRNA expression, most likely due to its high expression in normoxic conditions (Fig. 7F, H). Indeed, we could readily amplify both the G- and P-tWT1 isoforms from OVCAR3 cells in normoxia (Fig. 7G). We also tested another ovarian cancer cell line isolated from a primary tumor, and previously characterized by Sauriol et al. for having WT1 expression as determined by immuno-histochemistry staining from biopsies and Western Blot analyses of fresh isolates [69,70,71]. Contrary to the OVCAR3 cell lines, the TOV3291 cells had very little expression of tWT1 in normoxic conditions, as assessed by qPCR, but we observed a substantial and significant upregulation following LTHY adaptation (Fig. 7H, I). Despite differences in tWT1 induction between the two cell lines, they both engaged in an EMT-promoting transcriptional program following LTHY as determined by qPCR, with the primary tumor-derived TOV3291G cells displaying a more striking signature. (Fig. S7G).

Finally, we determined the prognostic values of WT1 and tWT1 for ovarian cancer patients using TCGA-OV. While overall survival probabilities couldn’t be predicted through WT1 expression, patients also expressing P-tWT1 seemed to display worse long-term survival probabilities (Fig. 7J). Dissection between overall and long-term survival probabilities was made possible by calculating survival significance using a sliding start date window, which shows a large window of significance past the minimal median survival date (1354 days). Using this approach, we were able to determine to determine that P-tWT1 expression is a significant negative prognostic marker in ovarian cancer for long-term survival (p < 0.05), but not overall survival (p = 0.12) (Fig. S7H).

Together, our data demonstrate the existence of a novel WT1 isoform (P-tWT1) in humans, which closely resembles the murine E6-tWt1 in mRNA structure but possesses a productive in-frame start codon within the additional intronic sequence similar to E7-tWt1, and that expression of this WT1 isoform correlates with a negative long-term outcome for ovarian cancer patients.

Discussion

There is a need to better understand tumor cell adaptation to sustained and severe hypoxia to grasp its impact on tumor cells and patient outcome. Here, we provide a new cell culture method, LTHY, developed to mimic the gradual onset of severe hypoxia, and recapitulate the conditions observed in vivo. Despite recent advancements in hypoxic incubation protocols, our method combines both duration and severity to mimic tumor onset and progression [9]. LTHY spontaneously engages EMT-like changes, which can be observed both morphologically and transcriptionally. However, these changes do not occur through pathways implicating known EMT external drivers such as TGFβ, signaling suppression, nor canonical EMT-associated transcription factors. Yet, expression of many EMT effector genes and miRNAs corroborates the initiation of EMT and agrees with previous work demonstrating that hypoxic adaptation, at 0.5% and below, induces an increase in cell motility in vivo suggestive of EMT [72].

Indeed, the EMT-like morphological changes observed at late stage LTHY were corroborated by with phenotypic changes such as the expression profiles of Vim, E-Cad and N-Cad and a clear EMT-promoting miR signature solidifying our assertion of spontaneous EMT [20,21,22,23,24,25]. In addition, we identified several other miRNAs with expression changes at the later stages of LTHY, but with unknown pathways linking them to our hypoxia-induced EMT-like signature. Such miRNAs include known suppressors of EMT, such as miR34b/c, shown to suppress EMT-like features in lung adenocarcinoma under normoxia, or TGFβ-dependent EMT regulators, such as miR-199a-5p [24, 73]. Furthermore, our analyses revealed the B16 cells did not differentially express the miR-200 family of miRNAs, which are known modulators of EMT [26]. These discrepancies may be due to the type of EMT induced during these assays, which may differ greatly from ours, and may reflect the different routes that cells take to induce EMT [74]. A combined analysis of miRNA expression, expected targeting, and mRNA expression is needed to both properly identify functional miRNAs to further elucidate their mode of action in LTHY-induced EMT.

Our work has also enabled the identification of a novel Wt1 isoform transcribed from a previously undescribed promoter region within intron 5 in both mouse and human loci, pointing to a conserved mechanism of induction. We show that the intronic promoter activity is HIF1-dependant in mice, with additional regulation provided by other factors. Additionally, this region coincides with a candidate cis-regulatory elements in both mice (EM10E0704920) and humans (EH38E1530575), further validating its functionality [75]. This finding identifies the second hypoxia-dependent WT1 promoter, and the first arising from an intronic region [60]. Intriguingly, induction of tWt1 expression occurred in the absence of additional increases in HIF1α stabilization, as assessed with our HIF1α-eGFP reporter line, suggesting additional rewiring of the transcriptional program beyond initial HIF1 activity. However, it is important to note that the level of HIF1 stabilization in the later stages of LTHY may be underestimated in our assay, as eGFP requires oxygenation to possess fluorescence activity [76, 77]. Nonetheless, the tWt1 intronic promoter was only active at 0.5% O2 and below, despite HIF1 being active at earlier time points. In fact, we see dramatic transcriptomic changes across oxygen conditions in our RNAseq datasets, despite stability in overall HIF1 levels, strongly suggesting additional layers of transcriptomic regulation in response to LTHY. This may provide an explanation as to how the expression of some HIF1 targets, like miR210, do not continue to increase as hypoxia becomes more severe. This may also be the result of epigenetic changes across LTHY, as a clear signature of effectors was identified in RNAseq, which may impact HIF1 activity, but would have to be further studied to be validated. However, this may not be the case for the tWt1 promoter, as our assay removes it from the local epigenetic context, yet it retained the transcriptional kinetics of tWt1 during LTHY. Finally, it may be that specific Hif1α/β post-translational modifications (PTMs) are driving different transcriptional preferences, as previously described [78].

Changes in HIF1 transcriptional activity are, however, not likely due to the effect of the dominant negative FIH (HIF3α), as Hif3α is not significantly expressed across LTHY. Less than 100 reads mapped to the Hif3a locus in any sample, with none spanning exon-exon junctions (data not shown). Alternatively, it may be that differences in transcriptional regulation are the result of a reduction in negative regulator activity, allowing for the de-repression of various genes, as was shown within the tWt1 promoter P2 sub-region. Nevertheless, HIF1 activity produces the dominant E7-tWt1 isoforms, where the novel splicing event introduces an intron 5-derived ATG with a strong Kozak context into the frame with the remaining WT1 CDS, and results in translated truncated Wt1 protein isoforms. Counterintuitively, this functional ATG is the second in the transcript, with the first ATG generating a small upstream ORF. Interestingly, upstream ORFs are a known mechanism for repressing normoxic translation of downstream ORFs while enhancing their translation under hypoxia. This may explain the hypoxia-dependent increase of E7-tWt1 expression, as this mechanism is known to also occur in the case of human EPO [79]. Finally, E7-tWT1 PTMs were identified using mass spectrometry, extended beyond those described in the literature, which could also confer hypoxic stabilization [80].

Our results also demonstrate that E7-tWt1, although heavily truncated, retains much of its function and regulates the expression of genes linked to gene transcription and cell-cell adhesion, two functional signatures also obtained by Ullmark and colleagues, using WT1 KTS(-) as bait in ChIPseq experiments [63]. However, investigating protein binding partners may shed additional light on E7-tWt1 functionality, as the lack of canonical N-terminus would alter the pool of interactors [81]. Additionally, our ChIPseq data suggests that tWt1 may be involved in hypoxia-induced EMT, as several genes linked to EMT were identified as targets, and WT1 is a known mediator of EMT. Finally, further investigation into E7K-tWt1 RNA binding is warranted, given the known RNA binding ability of KTS + WT1 isoforms and their implication in cancer progression [82].

We also provide compelling evidence of tWT1 isoform expression in human cancers as all human cancer cell lines tested either expressed them constitutively (MEL537, OVCAR3) or induced their expression during LTHY (MEL1300, SK-MEL23, ZR75, TOV3291G) suggesting a conserved mechanism of action. Further identification of a novel P-tWT1 isoform in AML and ovarian cancer adds to a long list of previously identified human WT1 isoforms, but only the second of its kind that stem from an intragenic TSS, as most isoforms arise from alternative splicing combinations [83]. Although P-tWT1 resembles murine E6-tWt1 in sequence arrangement with a continuous sequence from TSS into exon 6, it contains a potent in-frame ATG like E7-tWt1, which resulted in functional protein translation. This suggests a convergent evolution in cancer, where cancer cells from different species attempt to express a functional truncated version of WT1 through different mechanisms [84].

Finally, within TCGA-OV, tWT1 expression was found to be a negative prognostic marker for ovarian cancer for late-term survival using a new, non-biased approach enabling differential analysis of early and late survival probabilities. Curiously, TCGA-OV was the only TCGA dataset containing tWT1 expression, and correlated with the higher level of WT1 expression in this cancer type compared to others, where it was shown to promote EMT under hypoxic conditions [85, 86]. Identification of tWT1 only in TCGA-OV may be due to the prevalence of hypoxia in this cancer, as it is often diagnosed late into progression and therefore would have a higher degree of tumor hypoxia, thereby increasing the chances of obtaining biopsies derived from hypoxic microenvironments [87]. This is particularly important in the case of TCGA due to the sequencing depth per patient sample. In order to identify alternative transcripts, a higher sequencing depth is required than compared to gene-level expression analyses. Therefore, the TCGA database may be limiting both in terms of tumor microenvironmental representation, as well as transcriptomic representation. Deep RNAseq analyses enabling the discovery of alternate transcripts or mutations require a higher number of reads (typically more than 100 million reads per sample) [88]. While sufficient for gene-level expression analyses, the depth provided by TCGA samples may be insufficient for accurate tWT1 isoform calling [85]. As an example, we readily identified P-tWT1 in AML patient samples from the Leucegene dataset, which contains an average of 200 million read per sample, but not in AML samples from TCGA [65]. In conclusion, while further work is needed to elucidate the molecular tWT1 isoforms and their functions, its potential as a novel therapeutic target may be of particular interest for immunotherapy as the peptide obtained through translation of the added intronic sequence could provide a cancer-specific cryptic antigen [89].