Alternative polyadenylation dependent function of splicing factor SRSF3 contributes to cellular senescence

Down-regulation of SRSF3 leads to global shortening of 3′ UTR in human and mouse cells

To examine whether SRSF3-KD induces downstream changes other than alternative splicing, we firstly applied our published PA-seq protocol, which specifically enriched 3′ ends of mRNA by reverse transcription with modified oligo(dT) primer to capture the polyA tail and precisely identify polyadenylation site at the genome scale [56], to detect global APA changes in human 293T cells. Two biological replicates of lentivirus-mediated short hairpin RNA (shRNA) interference were performed and down-regulation of SRSF3 protein was confirmed by western blot (Fig. 1A, Fig. S1). The reliability of the identified pA sites was analyzed before comparing the dynamic changes of APA between SRSF3-KD and control cells. Known pA sites and those located at 3′ UTR regions were the top two categories of the identified pA sites (Fig. 1B), consistent with previous reports [32,56]. Besides, 85.5% of the identified pA sites were covered by PolyA_DB3 [58] and nucleotides composition near pA sites were in line with previous reports (Fig. S2). Moreover, canonical polyA signals (AAUAAA and AUUAAA) occupy ~75% of identified pA sites (Fig. S2C). These quality control results demonstrated the satisfied quality of the identified pA sites and their reliability for further analyses. The changes of pA site usage upon SRSF3 knockdown were next analyzed. Effective 3′ UTR (eUTR), which considering both location and abundance of pA sites for genes with APA, was used to reflect the weighted length of 3′ UTR for each gene [32,56]. Interestingly, an overall 3′ UTR shortening pattern evaluated by eUTR was observed in SRSF3-KD human 293T cells with two biological replicates (Fig. 1C), suggesting that SRSF3 downregulation favored the usage of proximal pA sites. We further examined the eUTR changes at individual gene level and found that SRSF3-KD induced more genes to use proximal pA sites in both replicates (Fig. 1D). Notably, a considerable proportion of overlapped genes between two biological replicates using PA-seq method and eUTR calculation further supported the reproducibility of such global trend (Fig. 1D).

Figure 1. SRSF3 downregulation leads to global shortening of 3′ UTR in human cells. (A) Western blot confirmed lentivirus-mediated RNA interference in both human 293T and HUVEC cells. GAPDH served as internal loading control. (B) Genomic distribution of pA sites identified in 293T cells based on PA-seq method. (C) Box plot of log₂-transformed eUTR based on PA-seq in control and SRSF3-KD 293T cells. The P value of t-test is shown. (D) Histogram of gene numbers with 3′ UTR shortening or lengthening upon SRSF3 KD at different cutoffs and overlapped genes with shortened 3′ UTR (with the cutoff of |ΔeUTR| > 50) between two biological replicates in 293T cells. |ΔeUTR| > 50, 100, 200 and 400 represent the absolute difference of eUTR between SRSF3-KD and control 293T cells, respectively. Number of shared genes (labelled Shared) between two biological replicates were also shown. (E) Box plot of RUD in 293T and HUVEC cells upon knockdown of SRSF3. (F) Histogram of gene numbers with 3′ UTR shortening or lengthening upon SRSF3 KD at different ΔRUD cutoffs. |ΔRUD| > 0.05, 0.1, 0.2 and 0.3 each represents a threshold of absolute difference of RUD between SRSF3-KD and control human cells. (G) Venn diagram of genes with shortened 3′ UTR based on different methods (eUTR and RUD), different shRNAs (sh1 and sh2) and different cells (293T and HUVEV) (ΔRUD ≤ -0.05) upon knockdown of SRSF3. (H) RNA-seq tracks of four representative genes in two human cell types upon SRSF3 KD. The transcription direction is shown at the bottom. The vertical red and blue arrows represent the proximal and distal pA sites, respectively. Y axis denotes the normalized read coverage. (I, J) qRT-PCR validation of the usage of longer 3′ UTR in the total expression (L/T) in both control and SRSF3-KD 293T cells of two biological replicates (rep1 in I and rep2 in J). Rep1 and rep2 represent two biological replicates, and sh1 and sh2 denote two different shRNAs. ** and *** mean P value less than 0.01 and 0.001 (t-test), respectively.

As an independent validation, we next adopted the method of RUD index [59], which reflected the relative usage of distal pA sites compared to total pA sites, to confirm the APA changes based on a separate RNA-seq data. Consistent with the results based on eUTR method, we detected a global reduction of RUD index upon SRSF3 KD in 293T cells (Fig. 1E), suggesting the favoring of proximal pA sites and shortening of 3′ UTRs. At individual gene level, we also detected more genes using shortened 3′ UTRs than lengthened ones in SRSF3-KD 293T cells (Fig. 1F). To expand this conclusion in more human cells, we applied the same RNA-seq and RUD analysis in Human Umbilical Vein Endothelial Cells (HUVECs), which is widely used as a vascular senescence model [32,60–62]. In line with the results in human 293T cells, knockdown SRSF3 with two replicates in HUVECs both displayed a similar 3′ UTR shortening trend at both genome-wide (Fig. 1E) and individual gene level (Fig. 1F).

To gain a comprehensive comparison of genes tending to use shorter 3′ UTRs upon SRSF3-KD based on different methods, biological replicates and types of cells, the interrelation of these gene sets was shown in a venn diagram (Fig. 1G). The majority (1134 genes) of SRSF3-KD induced 3′ UTR shortened genes were shared between two biological replicates of HUVECs (Fig. 1G). A considerable overlap (483 genes) between two different bioinformatical methods (eUTR and RUD) was also detected in 293T cells (Fig. 1G). Importantly, there were 355 genes showed 3′ UTR shortening in both 293T and HUVECs based on different methods and biological replicates (Fig. 1G), which were probably the common targets of SRSF3 in different cell types. SRSF3-KD induced 3′ UTR shortening in four representative genes was visualized in tracks of RNA-seq (Fig. 1H) and PA-seq (Fig. S3). Ten candidate genes were further selected for validation by reverse transcription coupled with quantitative real-time polymerase chain reaction (qRT-PCR), nine of which were confirmed to have reduced usage of distal pA sites (i.e., favor the proximal pA sites) in SRSF3-KD human cells (Fig. 1I-J). These above results indicated that downregulation of SRSF3 caused global 3′ UTR shortening in human cells.

To examine whether Srsf3 could play a similar APA regulatory role in mouse cells, we knocked down Srsf3 in mouse embryonic fibroblasts (MEFs) (Fig. S4A) and constructed RNA-seq libraries followed by RUD analysis. Consistent with the trend in human cells, knockdown of Srsf3 in MEFs also led to global shortening of 3′ UTRs (Fig. S4B) and the majority of genes with APA changes favored proximal pA sites (Fig. S4C). Both visualization of RNA-seq results and qRT-PCR validation of selected genes supported the shortening of 3′ UTR in Srsf3-KD mouse cells (Fig. S4D-F). Altogether, our results proved that downregulation of splicing factor SRSF3 resulted in global shortening of 3′ UTR in both human and mouse cells.

SRSF3 favors proximal pA sites binding and transcriptionally modulates APA

We next examined whether SRSF3 directly regulated alternative polyadenylation by integrative analysis of public SRSF3 CLIP-seq (crosslinking-immunoprecipitation and high-throughput sequencing) data [63,64] and our PA-seq and RNA-seq data before and after SRSF3 knockdown. As we focused on UTR-APA, CLIP signal located at 3′ UTRs was analyzed. Interestingly, the binding intensity of SRSF3 was significantly higher near proximal pA sites than distal ones for 3′ UTR shortened genes in both human and mouse cells (Fig. 2A). Since about 90% of genes had ≥ 100 nucleotides (nt) distance between proximal pA site and stop codon (Fig. S5), we analyzed CLIP signal within 100 nt around proximal or distal pA sites and observed same results (Fig. 2B). These results suggested that SRSF3 globally favors the proximal pA site binding. To confirm such result at individual gene level, three candidate genes (PTEN, a well-known tumor suppressor and related to longevity; DNMT3A, a known methyltransferase associated with aging and cancer; PIAS1, a repressor of transcription factor STAT1 that related to breast tumorigenesis), which were validated undergoing 3′ UTR shortening in SRSF3-KD cells (Fig. 1I-J, Fig. S4E-F), were visualized with their CLIP-seq and RNA-seq signal in UCSC genome browser. Noteworthy, SRSF3 had higher binding signal around the proximal pA site than the distal one for PTEN in all three biological replicates in mouse cells (see iCLIP of SRSF3 track in Fig. 2C, Fig. S6). SRSF3 iCLIP tracks of PIAS1 and DNMT3A showed similar proximal pA site preference (SFig. S7, S8). Consistent with the shortening of 3′ UTR, RNA-seq tracks of these three candidate genes showed considerable ratio change between alternative 3′ UTR (aUTR) and constitutive 3′ UTR (cUTR) in SRSF3-KD samples (Fig. 2C, Fig. S7, S8). Importantly, the 3′ UTRs of PTEN, PIAS1 and DNMT3A are all evolutionarily conserved and ranked at top 14%, 13.6% and 10.6% in all human coding genes, respectively (Fig. S9), implying the importance of such regulatory role of SRSF3 in an evolutionary view.

Figure 2. SRSF3 favors binding proximal pA sites and modulates APA at transcriptional level. (A-B) Box plots of public available human and mouse CLIP-seq data of SRSF3 in 3′ UTR shortened genes. Y axis represents the normalized tag intensity (reflected by RPM, reads per million) within 200 nt (A) or 100 nt (B) around the proximal and distal pA sites. Rep1 and rep2 means two biological replicates of HUVEC cells. (C) A combinated UCSC genome view near the 3′ UTR of mouse Pten containing PA-seq track, TargetScan predicted microRNA binding sties (TS miRNA sites), Mammal conservation score, SRSF3 iCLIP track (three replicates), RNA-seq track of control (Ctrl) and SRSF3-KD cells . cUTR and aUTR represent the common and alternative 3′ UTR of Pten, respectively. (D) Semi-quantitative RIP-PCR using cUTR or aUTR specific primer for PTEN on immunoprecipitated products separately harvested by anti-flag (SRSF3_flag) and IgG (Ctrl). IgG served as background binding (Ctrl). Left panel denotes the gel image while right panel represents the relative gray value quantified by imageJ software. (E) qRT-PCR quantifying the nascent RNA isolated by using the Click-iT kit in SRSF3-KD (sh1 and sh2) and control (Ctrl) 293T cells. PTEN_T, PTEN_L and PTEN_L/T represent total expression (short plus long 3′ UTR), expression of long 3′ UTR and relative expression ratio of long 3′ UTR compared to total expression, respectively. *, ** and *** mean P value less than 0.05, 0.01 and 0.001 (t-test), respectively.

Alternative polyadenylation contributing to different isoforms of PTEN has been reported by other research [65–67], however, SRSF3 directly binding to its 3′ UTR and regulating its APA is novel, we performed further experimental validations in human cell. RNA immunoprecipitation coupled with semi-quantitative PCR (RIP-PCR) showed higher binding signal of SRSF3 near the proximal pA site than the distal one of PTEN (Fig. 2D). These results indicated that SRSF3 regulated APA of PTEN by its binding preference to the proximal pA site. To further explore whether SRSF3 regulated PTEN’s APA at the transcriptional level, we applied analysis on nascent RNA. qRT-PCR showed that SRSF3-KD increased the usage of proximal pA site in nascent poly(A)+ RNA, indicating SRSF3 regulated APA of PTEN at transcriptional level (Fig. 2E). Together, SRSF3 favored proximal pA site binding of PTEN and regulated its APA at transcriptional level.

SRSF3-KD induced 3′ UTR-shortened genes enrich in senescence-associated pathways

To understand the functional consequence of SRSF3-KD induced 3′ UTR shortening, functional enrichment analyses were performed on those 3′ UTR shortened genes using gene ontology (GO) and KEGG pathway (Fig. 3A). We first analyzed 3′ UTR-shortened genes shared by two biological replicates of HUVEC, and discovered that four (cell division, protein ubiquitination, cell cycle and Wnt signaling pathway) out of the top ten enriched GO terms were associated with senescence (Fig. S10A) [68,69]. And seven out of the top ten enriched KEGG pathways (Endocytosis, Protein processing in endoplasmic reticulum, Ubiquitin mediated proteolysis, Insulin signaling pathway, mTOR signaling pathway, AMPK singling pathway and FoxO signaling pathway) were associated with senescence or aging (Fig. S10B) [70–73]. Next, we analyzed SRSF3-KD induced 3′ UTR shortening genes shared by 293T and HUVEC cells, and found that they were enriched in senescence-associated GO terms (cell division, cellular response to DNA damage stimulus, cell cycle and protein ubiquitination) and senescence/aging related pathways (Protein processing in endoplasmic reticulum, Ubiquitin mediated proteolysis, Endocytosis, FoxO signaling pathway and mTOR signaling pathway) (Fig. S11) [68,70,71,74]. 3′ UTR-shortened genes in MEFs were also enriched in senescence/aging related pathways (cell division, Ras signaling pathway, Wnt signaling pathway, Ubiquitin mediated proteolysis, PI3K-Akt signaling pathway and Endocytosis) (Fig. S12). Finally, we analyzed SRSF3-KD induced 3′ UTR shortening genes shared by 293T, HUVEC and MEF cells (221 genes showed in Fig. 3A), and the result showed that they were enriched in senescence-associated GO terms (cell division, cell cycle, insulin receptor signaling pathway and regulation of microtubule cytoskeleton organization) and senescence/aging related pathways (Protein processing in endoplasmic reticulum, FoxO signaling pathway, PI3K-Akt signaling pathway and AMPK signaling pathway) (Fig. 3B,C) [75–77]. These results above indicated that SRSF3-KD induced 3′ UTR-shortened genes possibly had the potential to function in senescence and aging in both human and mouse cells.

Figure 3. GO and KEGG enrichment analyses for 3′ UTR shortened genes shared by MEFs, 293T and HUVEC cells when knocking down SRSF3. (A) Venn diagram of 3′ UTR shortened genes (RUD-based) upon SRSF3-KD in different cells. Both the numbers and percentages were indicated. (B-C) GO term (B) and KEGG pathway (C) enrichment analysis for genes with 3′ UTR shortening shared by SRSF3-KD MEFs, 293T and HUVEC cells (221 genes in panel A). Red fonts represent functional categories related to senescence or aging.

Decreased SRSF3 causes senescence-related phenotypes in human and mouse cells

We next examined whether knockdown of SRSF3 could lead to senescence-associated phenotypes in human and mouse cells. RNA interferences using two shRNAs targeting SRSF3 caused increased senescence-associated β-galactosidase (SA-β-gal) staining [78] in both human (293T and HUVEC) and mouse (MEF and NIH3T3) cells (Fig. 4A). In addition, SRSF3-KD reduced cell growth rate in tested cell lines (Fig. 4B). Further investigation showed that SRSF3-KD resulted in cell cycle arrest in G2/M phase in 293T cells while arrest in G1 phase in MEFs (Fig. 4C). Notably, SRSF3-KD caused a common decrease of S phase percentage in both 293T and MEF cells (Fig. 4C). What’s more, knockdown of SRSF3 in human and mouse cells also led to the decreased expression of MKI67, a molecular marker for cell proliferation (Fig. 4D) [79]. In addition, SRSF3-KD resulted in upregulation of senescence-related marker CDKN1A (encodes p21) and/or CDKN1B (encodes p27) in human and mouse cells (Fig. 4D). These results demonstrated that knockdown of SRSF3 could induce senescence-related phenotypes in both human and mouse cells.

Figure 4. SRSF3 downregulation leads to senescence-associated phenotypes in both human and mouse cells. (A) SA-β-Gal staining before and after knockdown with two shRNAs (sh1 and sh2). (B) CCK-8 assay for cells with or without knockdown of SRSF3 in human and mouse cells. (C) Cell cycle analysis before and after knockdown of SRSF3 in 293T and MEF cells. (D) qRT-PCR revealed that knockdown of SRSF3 led to decreased expression of cell proliferation markers (MKI67, CDK1) and increased expression of senescence marker (CDKN1A or CDKN1B) in both human (293T and HUVEC) and mouse (MEFs and NIH3T3) cells. *, ** and *** mean P value less than 0.05, 0.01 and 0.001 (t-test), respectively.

SRSF3-KD induced 3′ UTR shortening genes promote senescence-related phenotypes via their increased protein levels

As knockdown of SRSF3 led to global shorting of 3′ UTRs and senescence-associated phenotypes, we hypothesized that 3′ UTR shortening mediated expression change of target genes can be an alternative mechanism in explaining SRSF3-KD induced senescence. Our results already showed that SRSF3 directly regulated APA of candidate genes including PTEN, PIAS1 and DNMT3A (Fig. 2, Fig. S7, S8). These three genes belong to enriched pathways associated with senescence/aging (PTEN belongs to pathways in cancer, PI3K-Akt signaling pathway; PIAS1 and DNMAT3A belong to transcription, DNA-templated, regulation of transcription). We thus chose these genes to test the hypothesis. To examine whether SRSF3-KD induced 3′ UTR shortening affects gene expression, we carried out dual luciferase assay with different lengths of 3′ UTRs. It has been reported that protein abundance of the variable PTEN isoforms resulting from alternative polyadenylation are distinct because of miRNA effects or difference in protein translation efficiency [65,66,80,81]. Our result showed that transcripts with the shorter 3′ UTR of PTEN can markedly produce more protein than those with the longer one in two different human cell types (Fig. 5A, Fig. S13). The result remained true in mouse cells (Fig. S14). In addition, shorter 3′ UTR of PIAS1 also generated more protein than the longer one (Fig. S15). Interestingly, multiple TargetScan (TS) predicted microRNA (miRNA) binding sites existed in the regions between proximal and distal pA sites for both PTEN and PIAS1 (see TS miRNA sites in Fig. 2C, Fig. S7). This result suggested that shortened 3′ UTR could enhance the protein expression through escaping from targeting by miRNAs. Further, there were more predicted miRNA binding sites within the alternative 3′ UTR of DNMT3A (see TS miRNA sites in Fig. S8), however, the dual luciferase assay cannot be performed due to the technical failure of cloning the longer 3′ UTR of DNMT3A. Next, RNA turnover rate analysis showed that transcripts with shorter 3′ UTR of PTEN was more stable than those with the longer one in HUVEC cells, though a less difference of such stability in 293T cells (Fig. 5B). These results suggested that SRSF3-KD induced shortening was likely to increase the protein production of affected genes, possibly contributed by miRNA-mediated stability control, translation efficiency or other mechanisms.

Figure 5. SRSF3-KD induced 3′ UTR shortening of PTEN produces more protein and contributes to senescence-associated phenotypes. (A) Dual luciferase assay for shorter (S) and longer (L) 3′ UTRs of PTEN in 293T (left) and HUVEC (right) cells. (B) RNA degradation curve of actD-inhibited transcription cells detected by qRT-PCR using primers specific for longer 3′ UTR (L) or targeting common region shared by longer and shorter 3′ UTR (T) of PTEN. (C, G) qRT-PCR indicated overexpression of PTEN (PTEN_OE and Pten_OE) led to decreased expression of cell proliferation marker MKI67 in 293T (C) and MEF (G) cells. (D, H) SA-β-Gal staining for 293T (D) and MEF (H) cells when overexpressing PTEN. (E, F, I, J) CCK-8 assay (E, I) and cell cycle analysis (F, J) for cells with or without overexpression (OE) of PTEN in 293T (E, F) and MEF (I, J) cells. *** and * mean P value less than 0.001 and 0.05, respectively (t-test).

We next mimicked the elevated expression of these 3′ UTR-shortened genes by overexpressing candidate genes and examined whether they could contribute to cell senescence. Overexpression of PTEN in human cells led to decreased expression of MKI67, a well-known cell proliferation marker (Fig. 5C). Upregulation of PTEN also caused senescence-related phenotypes in human cells including increased SA-β-gal activity (Fig. 5D), decreased cell growth rate (Fig. 5E) and reduced percentage of S phase cells (Fig. 5F). Additionally, cells transfected with PTEN shorter isoform grew slower than those with the longer one (Fig. S16). Importantly, upregulated Pten promoted alike senescence-associated phenotypes in mouse cells (Fig. 5G-J). Besides, we also proved that overexpression of Pias1 and Dnmt3a resulted in similar senescence-related phenotypes, including increased SA-β-gal activity, reduced cell proliferation rate and changed cell cycle (Fig. S17). All these results supported the notion that 3′ UTR shortening contributed to, at least in part, SRSF3-KD induced senescence.

PTEN is a well-known tumor suppressor and its overexpression extends mice lifespan through reduced PI3K activity and downstream cancer protection mechanisms [82], consistent with our observation that overexpression of PTEN induced cellular senescence (Fig. 5), an important tumor prevention mechanism [83]. Since PTEN can negatively regulate PI3K/AKT pathway through dephosphorylating phosphatidylinositol-3,4,5-trisphosphate (PIP₃) and thus reduce phosphorylated AKT (p-AKT) abundance [84,85], we next examined whether PTEN-induced senescence related to altered level of p-AKT. In good consistence with known reports, overexpressing PTEN in 293T and HUVEC cells reduced p-AKT abundance significantly while the AKT level did not exhibit a significant change (Fig. S18A). As knockdown of SRSF3 induced 3′ UTR shortening of PTEN and transcripts with shortened 3′ UTR of PTEN generated more protein than those with the longer one (validated by dual luciferase assay, Fig. 5A), one would expect that SRSF3-KD can increase the protein level of PTEN. Consistently, SRSF3 knockdown with two shRNAs both led to higher PTEN protein abundance in human 293T and HUVEC cells (Fig. S18B). Furthermore, SRSF3 knockdown attenuated the abundance of p-AKT but not that of the total AKT (Fig. S18B), coinciding with the result of PTEN upregulation. Together, SRSF3-KD induced senescence can be partially explained by PTEN upregulation, which at least in part contributed by 3′ UTR shortening.

Cell culture, transfection and selection of stable cell lines

All cells (293T, HUVEC, NIH3T3 and MEF) used in this study were cultured in DMEM medium supplemented with 10% FBS at 37 °C incubator with 5% CO₂. For transient transfection, cells were seeded in 6-well plate for 60%-70% confluence one day in advance, then transfected with lipofectamine 2000 (Invitrogen) in the second day. Cells were harvested for RNA and protein extraction 48 hours (hrs) after transfection.

For lentivirus transduction to stably knockdown SRSF3 or overexpress PTEN/PIAS1/DNMT3A in cells, the expression vector or the control vector plus VSVG and gag/pol encoding plasmids was transfected into the 293T cells, respectively. After culture for 24 and 48 hrs, the virus supernatant was harvested to infect cells. 24 hrs post the second infection, the medium was replaced with fresh DMEM medium supplemented with puromycin (final concentration was 2 μg/ml) to screen for stably transformed cells. After one day selection, the survival cells were trypsinized and split into two parts, one for cell proliferation assay and cell cycle analysis, the rest was cultured for additional two days in the medium with puromycin (2 μg/ml) for RNA and protein extraction.

Vector construction

For SRSF3 shRNA plasmid construction, the DNA oligonucleotides were annealed and then cloned into the pLKO.1 plasmid by using EcoRI and AgeI restriction enzyme sites. The short and long 3' UTR of PTEN/PIAS1 were PCR amplified by Q5 high-fidelity DNA polymerase (NEB) and then were individually ligated into the downstream of the Renilla luciferase coding gene in the dual luciferase vector psiCHECK-2. Firefly luciferase activity serves as the internal control of Renilla luciferase expression. To construct ectopic overexpression vector for PTEN/PIAS1/DNMT3A, the complete coding sequences were PCR amplified from human 293T cDNA or mouse 3T3 cDNA and then inserted into the pcDH_EF1_MCS_T2A_Puro vector.

qRT-PCR and western blot

Total RNA and protein were extracted with TRIzol reagent (Sigma) according to the manufacturer’s instruction. For cDNA synthesis, 1 μg total RNA was reverse-transcribed into cDNA by FastQuant RT Kit (TIANGEN) and oligo(dT) primer. Then quantitative PCR reaction was performed using 2×SYBR mix (KAPA) and the reaction was run on Bio-Rad CFX manager machine. Sequence information of all used primers was listed in Supplementary Table S1.

For western blot, the primary antibody for detecting SRSF3 (Abcam, cat. no. ab198291), PTEN (CST, cat. no. 9188T), pAKT (CST, cat. no. 4060T), AKT (CST, cat. no. 4691T) and GAPDH (CST, cat. no. 2118S) was 1:1000 diluted and incubated for 2 hrs at room temperature. The second antibody (HRP conjugate, YEASON) was 1:5000 diluted and incubated for 1 hr at room temperature. Blots were washed three times with TBST and visualization was carried out by Tanon Chemiluminescent Imaging System.

RNA stability assay

The stability for short and long isoforms of PTEN was measured by adding actinomycin D (actD) at a final concentration of 5 μg/ml into 293T and HUVEC cells to block transcription. Cells were harvested at different time points respectively. Total RNAs from all time points were then extracted by TRIzol reagent. The degradation rate was evaluated by qRT-PCR. The long isoform PTEN_L was quantified using primer pair specific to long 3′ UTR, while the overall expression of PTEN was quantified using primer pair shared by both short and long 3′ UTRs (PTEN_T).

Dual-luciferase reporter assay

Cells were transfected with psiCHECK-2 vectors containing either short-3' UTR or long-3' UTR using lipofectamine 2000 in 24-well plate. After transfection for 24 hrs, the firefly and Renilla luciferase activities were measured one by one according to the manufacture’s instruction (Promega). The final Renilla luciferase activity was normalized to the firefly luciferase signal.

Cell proliferation assay and cell cycle analysis

After selection by puromycin, the survival (transfected) cells were trypsinized and counted using hemocytometer. Then cells were diluted and seeded into a 96-well plate, with 2000 cells per well and three replicates for each time point (add CCK-8 reagent at 24 hrs, 48 hrs, 72 hrs and 96 hrs according to the manufacturer’s protocol). After each treatment, cells were incubated for two hours at 37 °C, then the optical density (OD) at 450 nm and 600 nm were measured respectively for each well by microplate reader (Tecan i-control).

For cell cycle analysis, cells were trypsinized, then centrifuged at 500g for four minutes (mins). Cell pellets were washed once with 1×PBS and resuspended with PBS containing 0.03% Triton X-100 and 50μg/ml Propidium Iodide (PI), and incubate for 10 mins. The cell cycle assay was then performed on the BD Flow Cytometer.

Nascent RNA assay

The nascent RNA assay was performed with the Click-iT method (Click-iT Nascent RNA Capture Kit from Thermo). Briefly, a nucleotide analog EU was added into the medium and cells were incubated for 1 hr at 37 °C. Total RNA was then extracted, and the EU-contained RNA was biotinylated. The EU-biotinylated RNA was isolated from the total RNA by Dynabeads MyOne streptavidin T1 (Invitrogen). Finally, the EU-RNA was quantified by qRT-PCR.

RNA immunoprecipitation coupled with semi-quantitative PCR (RIP-PCR)

Aspirate medium from one 10-cm plate 293T cells of 90% confluence, wherein SRSF3-flag is stably overexpressed. Wash cells with cold PBS once. Then irradiate cells under 200 mJ/cm² in UV linker. After UV cross-link, immediately wash cells one time with cold PBS, then add RIP lysis buffer (50 mM Tris pH 7.4, 250 mM NaCl, 5 mM EDTA, 1% NP40, 0.5 mM DTT, 1× protease inhibitor, 100 U/ml RNasin) to the plates, and incubate on ice for 20 min and collect cells in a new 1.5 ml tube. Then sonicate lysed cells using Biorupter, and spin down at 16000g for 10 min at 4 °C to collect the supernatant. Additionally, pre-incubate anti-flag antibody and Rabbit IgG with protein G beads at 4 °C for 4 hours (hrs) separately before harvest cell lysates. Then add one half supernatant to anti-flag-antibody-protein G beads, and the other half to IgG-protein G. After incubate and rotate at 4 °C for 4 hrs, put the tubes in the magnetic separation rack for 1 min. Aspirate the solution and wash beads three times with cold RIP lysis buffer. Then add 4mg/ml proteinase K solution to the beads and incubate for 20 min at 37 °C. Put the tubes in the magnetic separation rack once again, and then add Urea solution (100 mM Tris pH 7.4, 50 mM NaCl, 10 mM EDTA, 7M urea) and incubate another 20 mins at 37 °C. Finally, RNA was extracted by adding TRIzol reagent, and reversely transcribed into cDNA using FastQuant RT Kit (TIANGEN). Then the semi-quantitative PCR was performed using the primers specifically targeting the shared and long 3’ UTR regions of PTEN, respectively. Finally, the RIP-PCR result was detected by electrophoresis on 1% agarose gel.

PA-seq and RNA-seq library construction

PA-seq libraries for global profiling of 3′ UTR length was constructed according to our previous publication [56]. RNA-seq libraries for expression and RUD analysis were constructed according to the previous reports [57,90]. Both PA-seq and RNA-seq libraries were sequenced using Illumina HiSeq platform.

Bioinfomatical analysis methods

FastQC software was used for quality control of PA-seq and RNA-seq data (http://www.bioinformatics.bbsrc.ac. uk/projects/fastqc/), nucleotides with sequencing quality smaller than 20 were trimmed off. For PA-seq data, paired-end reads were first subjected to strand correction as previously described [56], and only reads heading with “TTT” were kept for further analysis. All the reads (strand-corrected PA-seq reads and quality-controlled RNA-seq reads) were aligned to the reference genome (mm9 for mouse data and hg38 for human data) using STAR [91]. The peak-calling, pA sites identification and localization were processed following our published methods [32,56]. eUTR methods [56] and RUD methods [59] were used for evaluating the changes in pA site usage before and after SRSF3 knockdown according to the published methods. For RUD analysis in MEF cells, we combined our previously identified pA sites [32] with the RNA-seq data in the present study.

Cufflinks was used to estimate the mRNA abundance using RNA-seq data (fragments per kilobase of transcript per million mapped reads, FPKM) by following previously published instructions [92]. Cuffdiff, a sub-package of cufflinks, was used to evaluate the gene expression changes.

UCSC genome browser was used for visualization of the PA-seq and RNA-seq data on specific genes. For functional enrichment analysis, the Database for Annotation, Visualization and Integrated Discovery (DAVID) [93] was used for the pathway enrichment analysis and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database was selected.

Data access

The raw PA-seq and RNA-seq data from this study can be accessed at the NCBI Sequence Read Archive (SRA; https://www.ncbi.nlm.nih.gov/sra/) with the accession number PRJNA523954 (or SRP186820).