A dynamic alternative splicing program regulates gene expression during terminal erythropoiesis

Human erythroblast populations

CD34+ cells were enriched from cord blood, cultured under conditions that promote erythroblast differentiation, and further enriched for stage-specific erythroblast populations by fluorescence-activated cell sorting (FACS) exactly as described (18). By gating narrow windows, high cell purity is attained for five discrete erythroblast populations (>90%), representing the last four cell divisions in terminal erythropoiesis. In some experiments, the cell populations enriched for proerythroblasts (proE) (culture day 9) and orthochromatic erythroblasts (orthoEs) (culture day 16) were treated with 100 μg/ml cycloheximide for 4 h to inhibit nonsense-mediated decay of RNA transcripts containing PTCs.

RNA-seq analysis

Total RNA was extracted using an RNeasy Plus Mini Kit (QIAGEN) from sorted human erythroblasts at distinct stages of erythroid maturation. The integrity of the RNA was evaluated on an Agilent Bioanalyzer, and samples with RNA integrity numbers (RIN) >8.0 were prepared for sequencing. Poly(A)-tailed RNA was subsequently prepared by the Epigenomics Core, Cornell Medical College, using the mRNA Seq Sample Prep Kit (Illumina Inc., San Diego, CA, USA) and used to create libraries for HiSeq2000 sequencing (Illumina).

Hundreds of millions of 50-nt RNA-seq reads were obtained from each of the 15 erythroblast samples, representing five distinct stages of differentiation from three independent sorting experiments. RNA-seq fragments were aligned to the Ensembl-annotated transcriptome version 67 (19) [hg19 human genome build GRCh37 (20)] using the Bowtie aligner (21). Read statistics are given in Supplementary Table S1. Transcript-level estimates were obtained using the transcript abundance estimation tool, eXpress (22), and were expressed as fragments per kilobase of exon per million fragments mapped (FPKM) as described previously (23,24). For each exon of every Ensembl gene, transcripts from the gene were divided into two sets: the set of isoforms including the exon (termed inclusion) and the set of isoforms with an intronic region spanning it (termed overlap). Expression of individual exons in ‘exon-inclusion’ isoforms relative to the total expression of all isoforms was represented as percent spliced in (PSI) and computed by calculating the ratio FPKM (inclusion)/FPKM (inclusion, overlap). Because only ratios of reads were compared, a library size normalization step between samples was not needed.

To classify exons as differentially expressed between the proE and orthoE samples, we used the estimated PSI values with the samr R package (25), assuming continuous data. This resulted in a test similar to a t-test, but where the null distribution was generated by a permutation test non-parametrically from the data. In the same manner, we estimated the PSI of each exon in the basophilic (basoE) and polychromatophilic (polyE) erythroblast populations. We then considered any exon for which the inclusion plus overlap FPKM was in the lower quartile to be insufficiently expressed and discarded it from the analysis. We also removed any exon for which a contributing isoform had a contradiction in coverage by having long spanning regions of 0 coverage (see Supplementary Methods). From this list filtered for low gene expression, we also filtered out exons with minimal alternative splicing (PSI <0.05 or >0.95) at any differentiation stage, because empirically we found that this PSI filter greatly reduced the incidence of false positives. Data for the PSI-filtered set of splicing events are shown in Supplementary Table S2, and the larger unfiltered PSI data set is provided in Supplementary Table S3. The filtered list of alternatively spliced exons was used as input into GOrilla (Gene Ontology enRIchment anaLysis and visuaLizAtion tool) for Gene Ontology (GO) analysis (26) (Table 1) and for analyzing stage-specificity of splicing switches (Figure 5).

Besides the computationally defined alternative splicing switches, a few additional examples were discovered in genes of interest by manual inspection of RNA-seq reads mapped to the human genome. Exons that did not computationally meet a ΔPSI threshold of 10% but showed strong evidence for stage-specific changes in the ratio of exon junction reads supporting inclusion versus exon exclusion reads were tested by reverse-transcriptase polymerase chain reaction (RT-PCR); examples of splicing switches detected in this manner are MEF2D, PICALM and MTMR3.

Validation of computationally predicted alternative exons

Many alternative splicing events were verified by manual inspection of mapped reads on the human genome browser, to filter out exons not supported by multiple exon-inclusion and exon-skipping reads. In selected cases, RT-PCR analysis was performed to visualize the amplified products corresponding to inclusion and skipping events. Total RNA was reverse-transcribed into cDNA using random primers and the Superscript III First Strand Synthesis System (Invitrogen), and PCR analysis was performed using AccuTaq polymerase (Sigma) using primers located in constitutive exons located upstream and downstream of each candidate alternative exon. Primer sequences are given in Supplementary Table S4. For initial RT-PCR validations, RNA from unsorted day 9 (proE-enriched) and day 16 (orthoE-enriched) cells was used; these results were subsequently confirmed with RNA purified from highly enriched (FACS-sorted) proEs and orthochromatophilic erythroblasts.

Differentiating erythroid cells execute a robust alternative splicing program

Highly purified human erythroblasts were obtained from in vitro cultures by FACS using differentiation stage-restricted cell surface markers as previously described (18). Stage-specific RNA-seq libraries were prepared from five morphologically distinct populations of successively more differentiated erythroblasts, each separated from the next by one cell division: proEs, early basophilic erythroblasts (e-basoE), late basophilic erythroblasts (l-basoE), polychromatophilic erythroblasts (polyE) and finally orthochromatophilic erythroblasts (orthoE), the last stage before enucleation (Figure 1). Three independent preparations of sorted cells were analyzed to provide biological replicates.

Alternative splicing events in differentiating erythroblasts were identified following the strategy shown in Figure 2. A total of 1.5 billion reads representing the transcriptomes of all 15 erythroblast samples were mapped to the Ensembl annotated transcriptome and analyzed using numerous tools to derive exon-level alternative splicing information as described in ‘Methods’ section. The initial set of 243 464 candidate alternative exons was filtered to remove low-expression genes, and exons with minimal alternative splicing, to derive a data set of 3784 alternative exons in 2250 genes (Supplementary Table S2). Validation of computationally predicted alternative splicing events was performed by manual inspection of RNA-seq reads mapped to the human genome and by RT-PCR analysis of the relevant transcript regions. Figure 3 illustrates representative alternative splicing events in genes involved in transcriptional regulation (NFE2L1, MEF2D, SOX6, ATRX, FOXM1, MAX), DNA and histone modifications (DNMT1, MBD1, CARM1, KDM4B) and signal transduction pathways (MAP2K7, MAP3K3, MAP3K7). The majority of these alternative exons did not substantially alter their splicing efficiency between proEs and orthoEs; i.e. similar results were observed by computational and experimental analyses independent of differentiation stage. However, alternative exons in NFE2L1, MEF2D and CARM1 were among those observed to splice differently as the cells matured (discussed below in Figure 4). Of interest, SOX6 and ATRX are important regulators of globin gene expression (27–29). These results demonstrate that differentiating erythroblasts execute an extensive alternative splicing program that can regulate protein structure and function via control of pre-mRNA splicing; moreover, when the affected proteins are transcription factors, important secondary effects on transcript levels of other genes might amplify downstream cellular consequences.

To investigate more broadly which biological processes are impacted by the erythroid splicing program, we performed GO analysis. GO terms significantly enriched among genes expressing major alternative splicing events, relative to all genes expressed (above the lowest quartile) in erythroblasts, are shown in Table 1. The top terms included numerous descriptors associated with chromatin structure and function, cell cycle regulation, organelle organization and regulation of RNA splicing. Among the read-validated alternative splicing events, we found at least 24 differentially expressed exons in 22 genes encoding enzymes that participate in histone modifications (30) (Supplementary Table S5), and at least 65 high-confidence alternative splicing events were detected in 48 genes encoding RNA binding proteins (Supplementary Table S6). As indicated in the tables, many of these alternative splicing events are not annotated among Refseq transcripts, and ∼40% are predicted to introduce PTCs that induce NMD. All of these splicing events passed low-expression and low-alternative splicing filters, and all were validated by RNA-seq read mapping. These observations suggest a high level of complexity in regulation of erythroid gene expression, and a strong potential that these splicing events have downstream effects on the abundance and structure of transcripts of numerous other genes. Thus, important biological pathways express a rich transcript diversity during late erythropoiesis.

Stage-specific switches in alternative splicing remodel the transcriptome during late erythroid differentiation

Alternative splicing switches can play key roles during differentiation and development by selective up- or downregulation of individual exons that alter function of the encoded protein(s). To determine the extent of splicing transitions during erythroid differentiation, we first compared PSI values for alternative exons in early (proE) and late (orthoE) erythroblast populations. In all, 1166 exons for which the difference in PSI values (ΔPSI) exceeded 10% were identified by this approach; 266 of these exhibited ΔPSI values exceeding 25%. A subset of splicing switches that alter protein coding domains was validated by RT-PCR using primers in the flanking constitutive exons (Figure 4). The previously described switch in protein 4.1R (EPB41) pre-mRNA splicing served as a positive control; as expected, splicing of exon 16 exhibited a substantial increase in PSI in orthoE cells compared with a much lower splicing efficiency in proE (Figure 4A). Similar to 4.1R, many of the newly discovered switches involve upregulation of splicing in orthoE (Figure 4A), but examples of downregulation (Figure 4B) and switching between mutually exclusive exons (Figure 4C) were also observed. For some of these, e.g. NDEL1, USO1, MEF2D and MTMR3, the ‘new’ isoforms with potentially new functions can be generated in orthoE that were absent or poorly expressed in proE. Except for USO1, PICALM1 and LGALS9, most of the genes with upregulated coding exons were also upregulated at the gene expression level, relative to global gene expression levels, further supporting the idea that the new isoforms might play important functions in late erythroblasts.

Consistent with a role for alternative splicing in important erythroid pathways, several validated switches in alternative splicing map to genes that function in cytoskeletal organization, cell division and chromatin function (Table 2). One of the biggest switches occurred in the NDEL1 (nuclear distribution factor E-homolog-like1) gene that is important for nuclear migration and nucleokinesis (31), while a less dramatic switch was predicted in survivin (BIRC5), a cytokinesis factor with a novel role in erythroblast enucleation (32,33). In the H2AFY gene, relative expression of mutually exclusive 100- and 91-nt exons reversed almost completely between proE and orthoE (Figure 4A). This switch alters the structure and function of the modified histone, macroH2A1, which can replace the canonical H2A to generate functional differences in chromatin activity (34–36). Consistent with the H2AFY splicing switch in terminal erythroid differentiation, differences in the relative abundance of the two isoforms have been reported in other tissues rich in proliferating versus differentiating cells (37). Other switches of likely functional importance occurred in kinases having roles in chromosome segregation and cytokinesis [NEK1, (38–40)] and microtubule- and actin-based cytoskeletal networks [MARK2, (41)] in a clathrin assembly gene (PICALM) associated with anemia and iron metabolism abnormalities in mouse mutants (42,43); and the mitotic assembly protein NUMA1.

To explore the hypothesis that alternative splicing networks can impact erythroblast remodeling, we examined the differentiation stage-specificity of splicing transitions. PSI as a function of erythroblast stage is shown in Figure 5A for several PCR-validated alternative exons that exhibited substantially increased inclusion between proE and orthoE (ΔPSI values ≥34). For these genes, most of the change in alternative splicing occurred in the last two (most mature) erythroblast populations, although lesser transitions in splicing efficiency occurred at earlier stages. Extending the analysis to the broader set of alternative exons, we next determined ΔPSI values across each of the four-stage transitions: proE to early basoE, early to late basoE, late basoE to polyE and polyE to orthoE. Figure 5B shows that ∼155 stage-specific splicing switches with ΔPSI > 25 were identified in these cultures, and the great majority of these occurred during the last two cell divisions. Of note, this is a conservative estimate that counts only exons having PSI values between 5 and 95 in all 15 samples assayed. These results demonstrate that most splicing transitions occur in late erythroblasts, temporally correlated with extensive cellular remodeling as the cells prepare to enucleate, and supporting the hypothesis that splicing changes play a determinative role in promoting late erythroid differentiation.

Alternative splicing coupled to NMD regulates expression of selected RNA processing factors

Alternative splicing coupled to NMD (AS-NMD) can regulate gene expression by splicing-mediated introduction of PTCs that induce nonsense-mediated degradation of the affected transcripts. PTC-containing transcripts can be generated by inclusion of exons that encode stop codons or alter translational reading frame (PTC-upon-exon inclusion), or by exon skipping events that alter reading frame (PTC-upon-exon skipping). Having noted that a number of RNA binding proteins express PTC transcripts in erythroblasts (Supplementary Table S4), we wondered whether AS-NMD was functioning to maintain homeostatic control of expression as described in previous studies (44–46). Instead, PSI and gene expression data indicated a different situation characterized by substantial stage-specific changes in relative expression of PTC transcripts (Figure 6). Increased proportions of PTC-containing transcript isoforms were expressed in late erythroblasts by either increases in exon inclusion or by increases in exon skipping (Figure 6A). Many of the predicted increases are substantial; e.g. the SNRNP70 gene switches from PSI = 13 to PSI = 43 for the smaller PTC-upon-inclusion exon. Similarly, well-known AS-NMD targets in the SR protein family including SRSF2, SRSF3, SRSF6 and SRSF7 also showed large ΔPSI switches from proE to orthoE (ΔPSI values of +12 to +47). Other classes of AS-NMD candidates include hnRNP proteins (D, L, LL and M), small nuclear ribonucleic particle proteins (SNRPA1, SNRNP70 and U2AF1), alternative splicing regulators [TRA2A (45), TRA2B] and a 3' processing factor (CPSF4). In a few cases, an increased proportion of PTC transcripts in late erythroblasts can occur in the context of PTC-upon-exon skipping events (Figure 6A, lower panel). SNRPA1, for example, expressed mainly full-length transcripts in proE as indicated by PSI = 94, but the decrease to PSI = 53 in orthoE predicts substantial expression of PTC isoforms in orthoE. However, it is important to note that some known [SRSF11, SNRPB, (45)] or candidate (TIAL1 and HNRNPD) PTC splicing events showed no evidence of stage-specific regulation (Figure 6B, upper panel), and a few (RHD, BCL2L12, CLK1 and CLK4) were regulated so as to decrease the proportion of PTC isoforms in orthoE (Figure 6B, lower panel). RT-PCR validation of splicing switches involving PTC exons enriched in orthoE or proE is provided in Figure 7. We propose that splicing-mediated generation of non-productive (PTC-containing) transcripts is an important post-transcriptional regulatory mechanism for controlling a selected subset of RNA processing factors in late-stage erythroblasts.

In principle, elevated expression of PTC transcripts could be due to increased production (splicing regulation) or to decreased degradation (NMD regulation). We reasoned that simple loss of NMD activity could not explain maturation-associated increases in PTC exons, as only selected PTC exons were upregulated in orthoE and a few were actually downregulated in these cells. This hypothesis was investigated experimentally by assaying whether inhibition of NMD could affect steady-state levels of PTC exons. If NMD is active in normal orthoE, then steady-state PSI values for PTC exons should increase due to selective stabilization of these transcripts in cycloheximide-treated erythroblasts inhibited for NMD. Figure 8 shows that PTC-upon-inclusion exons in SNRNP70 and HNRPLL had substantially elevated PSI values in NMD-inhibited cells, with PSI of a 72-nt exon in SNRNP70 increasing moderately in proE-enriched day 9 erythroblasts and dramatically in orthoE-enriched day 16 erythroblasts (upper panel). NMD inhibition also increased the proportion of PTC transcripts from the BCL1L12 and CLK4 genes that were generated by exon skipping and were more enriched in proEs (Figure 8, lower panel). We conclude that the NMD machinery is still active in late erythroblasts and therefore that the accumulation of PTC isoforms in these normal cells is not primarily due to loss of NMD, but instead must be due, at least in part, to regulation at the level of alternative splicing. Moreover, as the ‘missing’ quantity of PTC isoforms in normal cells must have been degraded, we propose that alternative splicing coupled to NMD (AS-NMD) is a mechanism for downregulating gene expression in late erythroblasts, analogous to the intron retention mechanism used by late-stage granulocytes to downregulate selected genes (47). Consistent with this, 9 of 11 RBP genes with upregulated PTC events (82%) were downregulated at the gene expression level relative to total mRNAs in late erythroblasts. In contrast, among genes shown in Figure 4 with validated upregulation of non-PTC exons in orthoE, 12 of 15 genes (80%) exhibited increases in gene expression level.

Supplementary Data

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