Identification of novel HPFH-like mutations by CRISPR base editing that elevate the expression of fetal hemoglobin

Fetal hemoglobin (HbF) is a tetramer consisting of two alpha-globin chains and two gamma-globin chains, which are highly expressed during the fetal stage of human life. The expression of HbF is silenced progressively after birth until it constitutes only about 1% of total hemoglobin (Bauer and Orkin, 2012). Naturally occurring mutations in the regulatory regions of the gamma-globin (HBG) genes have been shown to reactivate expression and increase HbF levels during adult life (Jacob and Raper, 1958). This inherited genetic condition is benign and is known as hereditary persistence of fetal hemoglobin (HPFH). Individuals, who inherit HPFH alongside other genetic disorders affecting the adult beta-globin gene, such as sickle cell disease or beta-thalassemia, were shown to have fewer, if any, symptoms (Jacob and Raper, 1958; Thein, 2018). Hence, high levels of HbF expression have been shown to be beneficial for improving the clinical outcomes of patients with sickle cell anemia and beta-thalassemia.

Genome editing approaches have largely focused on the beneficial effects of HPFH mutations to increase HbF levels in sickle cell disease (Traxler et al., 2016). These mutations either create de novo binding sites for erythroid activators or disrupt the binding sites of repressors, thereby increasing the expression of HbF. For example, the –175T > C, –198T > C and –113A > G HPFH point mutations create de novo binding sites for the erythroid master regulators TAL1, KLF1, and GATA1, respectively (Fischer and Nowock, 1990; Martyn et al., 2019; Stoming et al., 1989; Wienert et al., 2017; Wienert et al., 2015). Similarly, the introduction of HPFH-associated mutations around –115 bp from the transcription start site (TSS) of HBG (–114C > A, –117G > A, and a 13 bp deletion [∆13 bp]), and around –200 bp from the TSS (–195C > G, –196C > T, –197C > T, –201C > T and –202C > T/G), were shown to disrupt the binding sites of the two major fetal globin repressors, BCL11A and ZBTB7A/LRF, respectively (Martyn et al., 2018). However, the roles and locations of other regulatory elements in the HBG promoter that are involved in activation or de-repression are less well understood. Thus, tiling the HBG promoter using base editors could unravel molecular mechanisms of human hemoglobin switching and reveal additional point mutations that could be useful for therapeutic gamma-globin upregulation.

Targeted introduction of HPFH mutations into the HBG promoter by nuclease-mediated homology-directed repair is relatively inefficient and can result in high rates of random insertions and deletions (indels) through non-homologous end-joining DNA repair pathways (Cavazzana et al., 2017). In addition, due to the high homology between the duplicated HBG1 and HBG2 genes, simultaneous editing of both HBG promoters by programmable nucleases that cause double-stranded breaks (DSBs) sometimes results in ~4.9 kb deletion comprising the HBG intergenic region with uncertain consequences (Li et al., 2018; Métais et al., 2019; Wienert et al., 2017).

To overcome these limitations, we implemented a strategy to screen and identify potential regulatory mutations within the proximal promoters of the two human fetal globin genes HBG1 and HBG2. We employed CRISPR base editing to introduce an array of point mutations into the HBG promoters and then screen for those mutations that induce HbF to therapeutic levels, without the confounding effects of creating DSBs. Similar to previous findings, we observed that base editing using adenine and cytosine base editors (ABEs and CBEs, respectively) is highly efficient in creating point mutations without inducing high levels of indels (Gaudelli et al., 2017; Komor et al., 2016). We identified several novel point mutations that are associated with a significant increase in gamma-globin expression and could be of therapeutic interest. Our results demonstrated that base editors are a powerful tool for mapping the so far unknown regulatory elements within the HBG promoters and provide a proof-of-concept approach for the treatment of beta-hemoglobinopathies.

Previous studies have shown that the highly homologous HBG1 and HBG2 proximal promoters play a crucial role in the gamma-globin expression. Several non-deletional forms of HPFH-associated point mutations in the promoter region of HBG1 and HBG2 have been associated with increased expression of gamma-globin (Wienert et al., 2015). To identify novel regulatory elements in the human HBG promoters that influence gamma-globin expression, we performed a base editing screen to introduce point mutations in all compatible locations within 320 bp upstream of the TSS of the HBG genes. In brief, we created stable HUDEP-2 cells (Kurita et al., 2013) (an immortalized human erythroid progenitor cell line) expressing base editors (ABE or CBE), and then screened guide RNAs (gRNAs) targeting the proximal promoter region of HBG1 and HBG2 for their ability to upregulate fetal globin expression. The top gRNAs were validated for editing efficiency and HbF levels. Moreover, the plausible mechanism of novel gRNAs identified from the study on HbF elevation was further characterized by electrophoretic mobility shift assay (EMSA) and Chromatin immunoprecipitation quantitative PCR (ChIP-qPCR). Finally, the potential therapeutic induction of HbF levels for the identified novel gRNAs were validated in erythroid cells derived from healthy donor CD34+ HSPCs.

Base editors as a preferred genome editing tool for targeting the highly homologous HBG promoter region

First, we generated stable HUDEP-2 cell lines that express different gene editors, ABE, CBE, or Cas9, respectively. HUDEP-2 cells were transduced with ABE7.10 RA, BE3RA-FNLS, or Cas9 lentiviral constructs (hereafter named HUDEP-2-ABE, CBE, or Cas9). The vector copy number (VCN) of HUDEP-2 cells transduced with ABE, CBE, or Cas9 lentiviral constructs ranged from 0.25 to 0.85 by real-time PCR (Figure 1—figure supplement 1a). Previously defined sgRNAs targeting the BCL11A binding motif (Traxler et al., 2016) in the HBG1 and HBG2 promoters with a suitable editing window for ABE, CBE, and Cas9 were transduced with the VCN of 0.6–1.2 (Figure 1—figure supplement 1a). The editing efficiency was 88% for Cas9, 10–51% for ABE, and 59–73% for CBE, with the transduction efficiency (as measured by GFP expression) greater than 98% (Figure 1a). After differentiating the cells into erythroid progenitors, the percentage of HbF positive cells was higher in case of ABE and CBE than Cas9 (Figure 1b). While gene editing was very high with Cas9, we did not observe a corresponding increase in the HbF positive cells which might be due to a previously described 4.9 kb deletion comprising the HBG2 gene and HBG1-HBG2 intergenic region. It has been previously reported that introducing DSBs in highly homologous HBG promoters with Cas9 nucleases may generate this deletion (Li et al., 2018). Therefore, we determined the frequency of this deletions by qRT-PCR in all the edited samples. As expected, the 4.9 kb deletion was observed at a high frequency (76%) in Cas9 edited cells. We also noted some deletions in base edited samples but significantly fewer than with Cas9 (Figure 1c). Consistent with the frequency of the 4.9 kb deletion, the globin chain analysis by RP-HPLC showed lower levels of G gamma but not A gamma chain only in Cas9 edited cells in comparison to ABE and CBE edited cells after normalizing to the control (Figure 1d). These results suggest that ABE and CBE are highly efficient in editing the highly homologous regions like gamma-globin promoter without causing a large deletion between the two HBG genes.

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Screening of HBG proximal promoter with base editors identifies novel HPFH like mutations

To identify the potential regulatory regions involved in gamma-globin expression, we therefore selected ABE and CBE for tiling the HBG promoter. Transcriptomic analysis of the stable cell lines expressing ABE and CBE showed a significant correlation with the wild type HUDEP-2 cells, confirming that the gene expression profiles are not altered (Figure 1—figure supplement 1b). As the base editors and gRNAs are constitutively expressed, we determined the editing frequency of ABE and CBE stables with gRNA-2 for its effect on HbF elevation at different time points during expansion and differentiation. The editing efficiency and HbF levels in both ABE and CBE increases over time with no discernable effect on erythroid differentiation (Figure 1—figure supplement 1c-h). We then generated ABE and CBE gRNAs in all compatible locations up to 320 bp upstream of the TSS of the HBG1 and HBG2 promoters. Guide RNAs were designed with a suitable base editing window (target nucleotide in positions 3–9 from NGG PAM distal end) for ABE and CBE (Figure 2a). Among the 41 gRNAs designed, 36 gRNAs had a base editing window for ABE, and 32 gRNAs had a base editing window for CBE (Supplementary file 1). An overview of the methodology used in this study is illustrated in Figure 2b: All the gRNAs were cloned in a lentiviral vector with a GFP reporter. Lentivirus was produced for each gRNA; HUDEP-2-ABE and -CBE cells were then transduced in an arrayed format with equal transduction efficiency (~1 VCN/cell). The mean transduction efficiency for all these gRNAs in both ABE and CBE samples were around 97%. The gRNA transduced cells were then expanded for 8 days, successful base editing was then confirmed by NGS and Sanger sequencing.

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First, we determined the overall efficiency of ABE-induced A-to-G conversions and CBE induced C-to-T conversions at different target sites of HBG promoter by NGS. The gRNAs associated with lower base editing efficiency (<10%) were excluded from further analysis as they do not provide insights on HBG regulation (gRNAs -37, -38, -7, -18, -19, -29 in ABE, and gRNAs -1, -35, -37, -6, -7, -13, -19 in CBE) (data not shown). After excluding the low editing gRNAs, the total base editing efficiency (overall conversion achieved by a gRNA) varied from 12% to 81% and 13% to 80% for ABE (n = 30) and CBE (n = 25) respectively (Figure 2c and d) as determined by CRISPResso-2 analysis. The individual base conversion frequency (base conversion at single base pair resolution) of ABE (A:T to G:C) and CBE (C:G to T:A) ranged from 0% to 74% and 0% to 61%, respectively (Figure 2—figure supplement 1a and c). Sanger sequencing data analyzed by EditR further confirmed the base substitution efficiency at the target loci (Figure 2—figure supplement 1b and d). The base substitution efficiency for each gRNAs varied drastically depending on the base editing window for ABE and CBE. The average editing efficiency observed was high (>30%) in the canonical positions for ABE (A5-A7) and CBE (C5-C7), while it varied between 1% to 27% in the non-canonical positions for ABE (A1-A4, A8-A12) and CBE (C1-C4, C8-C16) for the different gRNAs used in this study (Figure 2—figure supplement 2e-f).

Subsequently, we explored the product purity and indel percentage for all gRNAs in ABE and CBE, as previous studies have shown that the base editors generate a low frequency of unintended edits at the target sites (Koblan et al., 2018). Most of the gRNAs in CBE transduced cells showed unanticipated C- to non-T edits (C-R/G-Y), among which C-to-G conversion was predominant. In the case of ABE, we observed a minimal level of unexpected base conversions (A-Y/T-R) at a few on-target sites, consistent with previous studies (Gaudelli et al., 2017; Komor et al., 2016, Figure 2—figure supplement 2a-b). The indel frequency obtained from deep sequencing data was less than 2% in both ABE and CBE (Figure 2—figure supplement 2c-d). Our results suggest that ABE exhibits higher product purity and lower indel frequency than CBE in all cases. In summary, we show that both ABE and CBE can effectively introduce A-to-G and C-to-T nucleotide substitutions respectively in the proximal promoters of HBG1 and HBG2.

To evaluate whether the targeted base substitution at the HBG promoter by using ABE or CBE has increased HbF expression, we analyzed the HbF positive cells in ABE and CBE edited cells by flow cytometry after intracellular HbF staining. The percentage of HbF positive cells ranged from 2% to 44% in ABE and 1% to 35% in CBE (Figure 2c–d). In our preliminary analysis, among the 30 gRNAs in ABE and 25 gRNAs in CBE that we have screened for editing the HBG promoter region, five gRNAs in ABE and one gRNA in CBE showed a greater increase in the number of HbF positive cells (in a range of 40–50%).

We identified several gRNAs in ABE (gRNA -39, -41, -42, -08) and in CBE (gRNA -33, -44, -38, -41, -40, -15, -17, -46, -20, -21) which have higher total editing efficiency (>40%) at the target site but resulted in low HbF level (<10%). We were curious to know whether these gRNAs can affect the binding sites of activators resulting in downregulation of gamma-globin expression. To test this hypothesis, we transduced the selected gRNAs in K562 cell lines stably expressing ABE or CBE, a cell model which has high basal level of HbF expression. The transduction efficiency was more than 98% and achieved a higher individual base editing efficiency for each of the gRNAs in both ABE and CBE (Figure 2—figure supplement 3a-b). However, we did not observe any decrease in the number of HbF positive cells (98% of cells were HbF positive) in any of the samples suggesting that the targeted regions did not have binding sites for essential transcriptional activators (Figure 2—figure supplement 3c-d).

Interestingly, some of the top candidates from the screen include target regions that were previously identified as binding sites for BCL11A (gRNA-2), KLF-1 (gRNA-4), and TAL-1 (gRNA-3), but we also identified a few other novel target sites (gRNA-10, gRNA-11, gRNA-15, gRNA-16, gRNA-21, gRNA-32, gRNA-34, gRNA-42). The gRNAs-2, -3, and -4 recreates the well-known naturally occurring HPFH mutations –114C > T, –117G > A, −175T > C and –198T > C (Liu et al., 2018; Martyn et al., 2018; Stoming et al., 1989; Wienert et al., 2018; Wienert et al., 2017). We compared the percentage of HbF positive cells with the editing efficiency for each of gRNAs at the target region in both ABE and CBE cells (Figure 2c–d). The total base editing efficiency was generally higher when compared to the proportion of HbF positive cells except in few cases (gRNA-3, -4, -13, -14, -37, and -38 in ABE edited cells). Together, the candidate gRNAs which upregulated HbF from the primary screening of the HBG promoter by ABE and CBE provides targets for further validation.

Base editing at potential target sites in the HBG promoter substantially induces HbF expression

The top eight gRNAs from the ABE screen (gRNAs -2, -3, -4, -10, -11, -15, -32, and -34) and the CBE screen (gRNAs -2, -10, -11, -16, -21, -32, -34, and -42) which resulted in the highest levels of HbF positive cells were further validated. Out of the top eight gRNAs identified from the base editor screen, five gRNAs (gRNA-2, gRNA-10, gRNA-11, gRNA-32, and gRNA-34) were common in both ABE and CBE, indicating that these target regions might play an important role in HBG silencing. The edited cells were cultured in erythroid differentiation media after the initial expansion, and a set of functional assays were carried out (Figure 2b). Corresponding to the screening results, the total editing efficiency ranged from 24% to 78% and 36% to 85% with mean transduction efficiencies of 96% and 90% for ABE and CBE, respectively (Figure 3a–b and Figure 3—figure supplement 1f). We observed individual base conversion of A- to-G (ranging from 0% to 65%) or C- to -T (ranging from 1% to 57%) at the respective target regions with less than 2% indel frequency (Figure 3—figure supplement 1a-b and e). Further, we also observed the undesired non-C-to-T conversions (i.e., C- to-A or C-to-G) at the on-target site by CBE but not with ABE (Figure 3—figure supplement 1a-b and Figure 3—figure supplement 2a-b). The distribution of specific nucleotide substitution mediated by ABE or CBE for all the top eight gRNAs are highlighted in Figure 3—figure supplement 2a-b, respectively. ABE showed higher base editing efficiencies of the cognate A and Ts (A113 and A116 for gRNA-02, T175 for gRNA-03, T198 for gRNA-04) than the bystander A and Ts (A110 and A112 for gRNA-02, T181 for gRNA-03, T199 for gRNA-04) for the creation of HPFH mutations. In the case of CBE, we also observed the C-to-T base conversion at the nucleotides adjacent to the protospacer sequence as previously observed (Arbab et al., 2020; Webber et al., 2019). One such example is gRNA-10 and gRNA-11 in CBE; we observed the base conversion outside the protospacer sequence (–117 site) in addition to on-target editing at –122 site within the base editing window (Figure 3—figure supplement 1b and Figure 3—figure supplement 2b). The base conversion at –117 site disrupts the core binding motif of the major fetal globin repressor – BCL11A (Martyn et al., 2018; Wienert et al., 2018; Yang et al., 2019). We distinguished the editing frequency in HBG1 and HBG2 promoters by phasing the edits with single nucleotide variations at positions −271, –307, –317, and –324 which are unique in HBG1 and HBG2 promoters, using Bowtie 2 and IGV software (Robinson, 2012; Langmead and Salzberg, 2013). Our analysis showed that base editing rates were highly similar and there is no variation in base substitution efficiency between the highly homologous HBG1 and HBG2 promoters (Figure 3—figure supplement 1c-d).

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We analyzed the HBG expression before and during differentiation by qRT-PCR. We observed a significant increase in the HBG mRNA expression for all the top eight gRNAs in ABE edited cells (p < 0.01 - p < 0.0001) (Figure 3—figure supplement 3a). In the case of CBE, gRNAs -2, -10, -11, and-42 showed a substantial increase in HBG mRNA expression (p < 0.05 - p < 0.0001), while gRNAs -16, -21, -32, and -34 showed a modest level of expression as compared with the control (Figure 3—figure supplement 3g) before differentiation. The globin mRNA expression pattern in both ABE (Figure 3—figure supplement 3b) and CBE (Figure 3—figure supplement 3h) edited cells also followed a similar trend during erythroid differentiation. We also determined the number of HbF positive cells before and after erythroid differentiation using FACS. As expected, the percentage of HbF positive cells in differentiated erythroid cells was slightly higher than that of the undifferentiated edited cells (Figure 3c–d). Further, we determined the effect of base editing on erythroid differentiation using flow cytometry analysis with CD235a and CD71 markers. The shift in expression of CD71 positive cells alone to CD71/CD235a double positive cells reflects the erythroid differentiation pattern of HUDEP-2 cells (Kurita et al., 2013). The percentage of double positive cells was 83–90% for CBE and above 95% for ABE edited cells compared to the control, which was 77% and 97%, respectively, suggesting that the differentiation ability of the edited cells was not affected (Figure 3—figure supplement 3d and Figure 3—figure supplement 3j).

Furthermore, the level of globin chains was analyzed by using reverse-phase HPLC in differentiated erythroid cells from both ABE (Figure 3—figure supplement 3c) and CBE edited samples (Figure 3—figure supplement 3i). We observed a significant induction of gamma-globin chain expression, which represented 10% to30% of total beta-like globin content in all the ABE edited samples (gRNAs -2, -3, -4, -10, -11, -15, -32, and -34) (Figure 3g). In the case of CBE, the gamma-globin chain levels were around 6% to45% of total beta-like globin content, among which gRNAs -2, -42, -10, and -11 showed significant elevation when compared to the control (Figure 3h). In both ABE and CBE edited cells, the increase in gamma-globin chains was consistently associated with a reciprocal reduction in beta-globin chains thereby maintaining the alpha to beta-like globin chain ratio (Figure 3—figure supplement 3c and Figure 3—figure supplement 3i). Even though the HBG1 and HBG2 promoters were base edited with equal efficiency, HBG1 showed moderately higher expression levels compared to HBG2 in most of the ABE and CBE edited cells. The decrease in HBG2 expression in these samples might be due to biased HbF regulation or the 4.9 kb deletion that deletes the HBG2 gene.

To find whether decrease in HBG2 expression is due to the 4.9 kb deletion, gRNAs which showed significant reduction in HBG2 over HBG1 expression in ABE (gRNA-4, -10, -11, -15, -32, -34) and CBE (gRNA-2, -10, -11, -32, -34, -42) edited cells were further investigated by qRT-PCR. Interestingly, the frequency of 4.9 kb deletion in ABE ranged from 2% to 32% while in CBE it ranged from 0% to 12% (Figure 3—figure supplement 3e and Figure 3—figure supplement 3k). To determine the correlation between the reduction in HBG2 chain expression and the frequency of large deletion, Pearson correlation analysis was performed in the above-mentioned gRNAs in ABE and CBE edited cells. We observed a high correlation (r = 0.71) in the case of ABE, whereas much lower correlation was observed with CBE (r = 0.26) (Figure 3—figure supplement 3f and Figure 3—figure supplement 3i). These data suggest that the reduction in G gamma chain expression is due to higher frequency of deletions in the ABE edited samples, while in the case of CBE, the decrease in the G gamma chain expression is independent of larger deletions and might be due to the biased expression of gamma-globin.

We observed a substantial difference in deletion rates across gRNAs as well as between base editors (Figure 3—figure supplement 3e and Figure 3—figure supplement 3k). The difference in the DNA sequence composition which often affects the editing efficiency might also be responsible for varied deletion observed across the gRNAs targeting the HBG promoter. On the other hand, processivity of the editors could account for the difference in deletion observed with the same gRNA while editing with CBE and ABE. As the base editors cannot dock on an already edited strand, CBE with a higher rate of editing (~50% editing on day 1) is prevented from interacting again with the DNA, thus reducing the chances of deletion compared to ABE which takes longer to achieve similar editing (~50% editing on day 8) (Figure 1—figure supplement 1b and c). This observation is further supported by the minimal deletion seen in samples edited with ABE8e which has a higher processivity (~90% editing within 24 hr) compared to both CBE and ABE 7.10 (Figure 3—figure supplement 4c, Richter et al., 2020).

Next, we analyzed the level of hemoglobin tetramers in the differentiated cells by HPLC to determine whether increase in HBG chain expression resulted in functional HbF production. We observed a significant induction of HbF in all the top eight target gRNA transduced cells in ABE (gRNAs- 2, -3, -4, -10, -11, -15, -32, and -34), whereas only gRNA-2, -10, -11, and -42 expressed higher levels of HbF in CBE edited cells (Figure 3e–f). Consistent with globin chain analysis, we observed that the increase in HbF variant is associated with compensatory downregulation of adult hemoglobin levels in the edited cells. The relationship between the editing efficiency and HbF expression was analyzed for all the top-scoring gRNAs in ABE and CBE edited cells (Figure 3i). Among the validated top eight gRNAs in ABE and CBE, gRNA-2, -10, -11 with ABE and gRNA-2 with CBE resulted in a high target editing efficiency with a corresponding increase in HbF expression. In case of gRNA-42 with CBE, only a modest level of HbF elevation was achieved even with higher editing efficiency. On the other hand, gRNAs -3 and -4 with ABE and gRNAs -10 and -11 with CBE showed higher elevation of HbF levels despite lower base conversion efficiency. The higher number of HbF positive cells with minimal base editing might be due to heterogenous editing at target site per cell since there are two copies each of HBG1 and HBG2. Further, irrespective of editing at the target region, the binding of CRISPR-Cas9 complex through the gRNAs at the HBG promoter might disrupt the binding of major transcriptional repressor that are involved in globin expression (Shariati et al., 2019).

Among the samples which resulted in higher HbF induction with lower editing efficiency, we validated gRNA-03 and -11 with a hyperactive variant of ABE (ABE8e) to determine whether further elevation in HbF level can be attained by increasing the editing efficiency. The HUDEP-2 cells stably expressing ABE8e were transduced with gRNA-03 and -11, with a VCN of 0.28% for the editor and 0.56% for the gRNA (Figure 3—figure supplement 4e). We observed a high percentage of base substitution at the target site (more than 95%) with the corresponding increase in the HbF positive cells and gamma-globin chains in both the gRNAs (Figure 3—figure supplement 4a-c). The erythroid differentiation capacity of the edited cells was equivalent to that of control (Figure 3—figure supplement 4b). The frequency of larger deletions was also significantly reduced perhaps because of the higher processive rate of ABE8e (Figure 3—figure supplement 4d). Thus, the ABE8e variant has improved the base editing efficiency at the target region and provided higher level of HbF induction with a reduced frequency of larger deletion.

Through this screen, we identified multiple novel individual regulatory regions and validated well-known HPFH mutations in the HBG proximal promoter that are important for gamma-globin regulation. Interestingly, gRNA-2 (with ABE or CBE) and gRNA-4 (with ABE) disrupt the binding site for the major gamma-globin repressors, BCL11A and LRF/ZBTB7A, and generate the binding motif for the transcriptional activators, GATA1 and KLF1, thus resulting in overall activation of HBG expression. Base editing of –114C > T, –115C > T and –116A > G mutations disrupts the binding of BCL11A and the base conversion at –198T > C and –199T > C affect the binding of LRF/ZBTB7A to the HBG promoter. Further, the installation of –113A > G and –198T > C mutations by gRNA-2 and gRNA-4, generate a binding site for GATA1 and KLF-1, respectively. Moreover, the base conversion at −175T > C by gRNA-3 (with ABE) creates a TAL1 binding site. The novel gamma-globin regulatory region identified includes the target base substitution mediated by gRNA-10, -11, -15, -21, -32, and -34 with ABE or CBE. With gRNAs -10 and -11, ABE converts nucleotide at –123T > C and –124T > C position, whereas CBE converts nucleotide at –122G > A (on-target editing site) and –117G > A (outside the editing window) positions. Target base substitutions at –123T > C and –124T > C positions result in greater induction of HBG expression, equivalent to that of the known HPFH mutations that disrupt the binding of BCL11A. Overall, the potential gRNAs include gRNA-2, 3-, -4, -10, and -11 with ABE and gRNA-2 with CBE that exhibit high induction of HbF expression.

Base editing of the –123 region of HBG promoter in human CD34+ HSPCs

To further determine the therapeutic potential of novel targets identified from this study on induction of gamma-globin expression, we performed base editing of CD34+ HSPCs from healthy donor (Figure 4a). Electroporation of the ABE8e mRNA with gRNA targeting the BCL11A binding site (gRNA-2) and –123 novel cluster (gRNA-11) effectively generated highly efficient base editing at the target site. The editing efficiency observed at individual base positions were –110 (31%), –112 (37%), –113 (80%), and –116 (66%) with gRNA-2 and –123 (89%), –124 (91%) with gRNA-11 (Figure 4b). In case of gRNA-11, the base editing events generated a high proportion of –123 and –124 mutations in combination at the target site. We cultured the base edited CD34+ HSPCs under erythroid differentiation conditions and analyzed HbF expression. The relative levels of HBG expression were significantly higher in gRNA-11 (>6-fold) and gRNA-2 (>5-fold) edited samples when compared to control (AAVS1 edited sample) by qRT-PCR (Figure 4d). In contrast, a significant downregulation of HBB and unchanged levels of HBA expression were observed in both the tested targets. Similarly, we observed a substantial increase in HbF protein expression in erythroblast derived from base edited CD34+ HSPCs. Flow cytometry and HPLC variant analysis confirmed the robust increase in the proportion of HbF positive cells and their HbF content compared with control samples for all the tested targets, with the higher effect in gRNA-11 (Figure 4e and g). The globin chain analysis showed an increase in expression of HBG1 and HBG2 globin chain levels and a reduction of HBB globin chain level (Figure 4f). Importantly, base editing of the HBG proximal promoter with gRNA-2 or -11 did not alter enucleation potential or the expression of erythroid maturation markers CD235a or CD71 (Figure 4h–i). Finally, we determined the frequency of the 4.9 kb deletion in CD34+ HSPCs electroporated with ABE8e and gRNA-2 or -11. We observed a very minimal frequency of the 4.9 kb deletion which might be due to higher processivity and transient expression of the base editor mRNA (Figure 4c). The present results suggest that the level of HbF induction mediated by the installation of novel –123 cluster HPFH-like mutations (through gRNA-11) is comparable to the naturally occurring –115 cluster HPFH mutations (through gRNA-2) that disrupt the binding site of BCL11A. Together, our data demonstrate that adenine base editing of the HBG1 and HBG2 promoters to recreate the novel –123 cluster HPFH-like mutations is a potential approach for the therapeutic induction of fetal globin level and treatment for beta-hemoglobinopathies.

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The –123 T>C and –124 T>C HPFH-like mutations creates a de novo binding site for KLF1

Finally, we investigated the possibility that novel HPFH-like mutations introduced by the base editor might either create or disrupt the binding site for transcriptional regulators. Interestingly, we observed that the base editing at –123T > C and –124T > C sites by ABE with a single gRNA creates the consensus binding site for the master erythroid transcription factor KLF1 (Figure 5a and b; Tallack et al., 2010). We performed EMSA to verify binding of KLF1 to a probe containing this core element. We observed modest but clear binding of KLF1 to the –123T > C and –124T > C mutated probe in EMSA but not with the wild type probe (Figure 5—figure supplement 1a-b) or probes containing either –123T > C or –124T > C mutations alone (Figure 5c–d). This confirms that the combination of –123T > C and –124T > C mutation is important for the KLF1 binding to the HBG promoter. Next, we performed ChIP experiments to determine whether the KLF-1 directly interacts with –123T > C and –124T > C mutated region of HBG promoter in vivo. The KLF1 ChIP was performed in three independent HUDEP-2 clones sorted from wild type cells or the double mutant edited cells, respectively. The ChIP results were normalized to an unrelated positive control, a KLF1 binding site at the SP1 locus. We observed a weak increase in the signal of KLF1 binding to the HBG promoters in the cells edited to contain the –123 and –124 mutations, but the effect was modest, and a similarly weak enhancement was also observed at an arbitrary negative control locus, VEGF (Figure 5—figure supplement 1c). Thus, as seen in the EMSA, the KLF1 binding is at best weak and may be below the level of detection by ChIP. Future investigations would be required to confirm that KLF1 binding to this site is the main in vivo mechanism of –123T > C and –124T > C HPFH driven upregulation of gamma-globin.

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Figure 5—source data 1

Electrophoretic mobility shift assay (EMSA) showing KLF1 binding to –123T > C/–124T > C probe but failing to bind to –124T > C probe, –123T > C probe, and wild type (WT) probe with the –123T/–124T region of the HBG promoter in vitro.

Lanes 1, 4, 7, and 10 contain nuclear extracts from COS cells transfected with a pcDNA3 empty vector. Lanes 2–3, 5–6, 8–9, and 11–12 contain nuclear extracts from COS cells overexpressing KLF1. Binding of KLF1 to the –123T > C/–124T > C hereditary persistence of fetal hemoglobin (HPFH) mutant probe can be observed in lane 11, with a super shift of KLF1 in the presence of anti-KLF1 antibody in lane 12.

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Off-target and gene expression analysis after base editing at the HBG promoter

ABE and CBE are known to create Cas-dependent DNA off-target and transient Cas-independent RNA off-target at low levels (Anzalone et al., 2020). It has been reported that the Cas-independent DNA off-target is very low and undetectable (Anzalone et al., 2020). We used Cas-OFFinder tool to predict the Cas-dependent DNA off-target for the novel gRNA (gRNA-11). The identified target regions were deep sequenced by NGS. Despite the higher on-target efficiency, off-target editing was not observed at the top target sites (Figure 6a). Next, we performed transcriptome-wide RNA sequencing on ABE and CBE stables with or without gRNA-2 and -11 to check whether base editing induced major spurious RNA deamination. The distribution frequency of A-to- I (in ABE) or C-to-U (in CBE) conversion across the base edited samples was very similar to that of the parental stable cell line (Figure 6b–e). To further verify that editing the gamma-globin promoter is not affecting the expression of other genes involved in globin regulation, we performed the differential analysis on these samples for the specific genes involved in globin regulation. We observed that there is no significant difference between the edited and control cells except for both gamma- and delta-globin genes (Figure 6—figure supplement 1).

Figure 6 with 1 supplement see all

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Overall, these results support that the ABE and CBE are useful in creating specific point mutations in the homologous HBG1 and HBG2 promoters, leading to a potential increase in the number of HbF positive cells and overall HbF production with a significant reduction in the larger deletion frequency.

During normal globin switching, interactions of cis-acting elements with several different transcription factors lead to the silencing of fetal globin and in turn the activation of beta-globin (Ikuta et al., 1996). To obtain insights into the regulation of gamma-globin gene expression, we have used two complementary base editing approaches to screen the HBG promoter at single nucleotide resolution. This approach allowed us to identify several novel nucleotide substitutions in the HBG promoter that elevate HbF levels by altering the binding site for transcriptional activators or repressors.

Current approaches to studying fetal globin regulation by programmable nucleases often result in the deletion of the HBG2 gene due to the introduction of DSBs in both HBG promoters (Traxler et al., 2016). The elimination of the 4.9 kb intergenic region (including the HBG2 gene) appears to allow the locus control region (LCR) to directly interact with the HBG1 promoter and drive its expression (Métais et al., 2019). It can be challenging to determine the exact role of different HPFH mutations on individual gamma-globin expression because mutations can occur in either or both HBG2 and HBG1 promoters. Further, the CRISPR-Cas9-based editing produces different combination of indel at the target sites which makes it difficult to pinpoint the precise mutations involved in the gene regulation. A base editing strategy converts target bases in the editing window without the generation of DSBs and hence largely avoids splicing of the HBG locus. Using this strategy, we targeted regions in both HBG1 and HBG2 promoters and were able to efficiently edit sites in the promoters with fewer or no large deletions, which gave us the opportunity to evaluate gamma-globin expression from two active promoters.

We did observe a small percentage of 4.9 kb deletions even with base editors that use a nickase variant of the CRISPR/Cas9 system in our study. The larger deletions may be mainly a result of simultaneous CRISPR-Cas9-induced DSBs or by paired nickase-mediated two single-strand breaks (SSBs) on opposite DNA strands of the HBG1 and HBG2 gene (Ran et al., 2013a). Interestingly, recent studies have shown the possibility of adjacent SSB on the same DNA strand leading to the formation of genomic deletions in plants. The deletion frequency depends upon the initial release of the single-stranded fragment between the two SSBs (Schiml et al., 2016). Further reports suggest the conversion of the persistent nick into DSBs by the replication fork. The R-loop primed replication fork encounters the single-strand nick site in DNA template and collapses to produce a DSB (Kuzminov, 2001; Wimberly et al., 2013). Based on these findings, we predict the concurrent introduction of SSB by base editors at the editing site of the HBG1 and HBG2 promoter might generate some 4.9 kb larger deletions, though we observed very few.

Several different HPFH point mutations have been reported in the HBG promoters; and the effect of these mutations on gamma-globin expression in the native cellular environment has been deciphered for this limited set of mutations (Bauer and Orkin, 2012; Liu et al., 2018; Martyn et al., 2019; Wienert et al., 2017; Wienert et al., 2015). Our findings are in agreement with previous reports that the point mutations in three different regions of the HBG promoters centered around positions −198, –175, and −115 mimic the HPFH-associated point mutations affecting essential regulators of HbF expression (Liu et al., 2018; Martyn et al., 2019; Martyn et al., 2018; Stoming et al., 1989; Wienert et al., 2017; Wienert et al., 2015). Among the known HPFH point mutations, base conversion within the –115 cluster (from –110 to –116) showed the highest increase in promoter activity, confirming previous studies (Fucharoen et al., 1990; Gilman et al., 1988; Zertal-Zidani et al., 1999; Motum et al., 1994). CBE-mediated base conversion (C- to- T) at positions –114 and –115 resulted in a significantly greater induction of HbF than the multiple A-to-G nucleotide substitutions at −110, –112, −113, and –116 positions made by ABE. Recently, it has been shown that the major HbF repressor BCL11A directly binds to the core TGACC motif located at – 114 to –118 (Liu et al., 2018; Martyn et al., 2018). Naturally occurring HPFH mutations at –117G > A, –114C > A, –114C > T, –114C > G, and Δ13bp disrupts binding of BCL11A to the promoter (Martyn et al., 2018). The –113A > G HPFH mutation within the –115 cluster creates a binding site for the master erythroid regulator GATA1 without disrupting the binding of BCL11A (Martyn et al., 2019). Our results are consistent with these previous reports showing that disruption of the core binding region of BCL11A and the creation of a de novo binding sites for GATA1 results in the elevation of fetal globin in wild type HUDEP-2 cells (Wienert et al., 2017). ABE-mediated T-to-C substitution at position –198 of the HBG gene promoter has previously been shown to be associated with British HPFH and substantially elevates expression of HbF by creating a de novo binding site for the erythroid gene activator KLF1 (Tate et al., 1986; Wienert et al., 2017). Another known HPFH mutation (–175T > C) has been shown to promote enhancer looping to the HBG promoter through recruitment of the activator TAL1 (Wienert et al., 2015). Further, increased editing efficiency at the –175T > C position with the hyperactive variants of ABE (ABE8e) resulted in the highest induction of HbF synthesis in human erythroid cells.

In this study, we have identified several new point mutations in the HBG promoter associated with high HbF levels. HBG promoter base editing by ABE-mediated conversion (A- to-G) revealed multiple potential HbF regulatory regions compared to CBE since the targeted region had more ABE-compatible gRNAs than CBE. In addition to the known mutations, we have identified novel substitutions at –69 (C -to- T), –70 (C- to- T), –122 (G -to -A), –123 (T -to- C), and –124 (T -to -C) of the HBG promoters as potential new regulatory mutations that can elevate gamma-globin expression. The levels of gamma-globin expression resulting from these mutations were very similar to those of well-characterized, naturally occurring HPFH mutations. Our study has predicted that nucleotide substitutions at –123T > C and –124T > C positions of the HBG promoter might result in reactivation of gamma-globin expression through the creation of a binding site for KLF-1, which was then confirmed by EMSA. This result, together with the observation that a de novo KLF1 site formed by the –198T-to-C mutation can upregulate fetal globin (Tate et al., 1986; Wienert et al., 2017) raises the possibility that introduction of a KLF1 binding site anywhere around the HBG promoter could potentially upregulate HBG gene expression. In contrast to our finding in EMSA, we observed only a very weak signal for the binding of KLF1 at the edited site of the HBG promoter by ChIP. Thus, our hypothesis, primarily on the basis of observing in vitro binding of KLF1 in EMSAs, is that the –123 and –124 mutations create a new KLF1 binding site, that is relatively weak and difficult to detect using ChIP but other hypotheses are possible. For instance, it could create a binding site for another activator. The relative proximity of this site to the BCL11A site, that begins around –117, suggests it may also directly or indirectly affect BCL11A binding. Further work needs to be done to assess these possibilities.

The current screening approaches that we used to identify the regulatory element in the proximal promoter of HBG is limited by several technical issues. The availability of NGG PAM sequences in the target region confines the resolution of the screening approach. The editing efficiency for ABE7.10 RA or BE3RA-FNLS is not uniform across the target regions (Koblan et al., 2018). The effect of transverse mutation in the target region on gene regulation is not possible as the current base editors are mainly involved in the installation of transition mutations (Gaudelli et al., 2017; Komor et al., 2016). The bystander mutation introduced by the base editors at the target regions makes it difficult to identify functional regulatory single nucleotides responsible for the gamma-globin regulation. These limitations can be overcome by the use of several different strategies including the use of alternative base editor variants that recognize the non-canonical PAM site (Richter et al., 2020). In addition, recently developed hyperactive variant of base editors will improve the increasing editing efficiency at the target site with the broader editing window (Richter et al., 2020). The scope of this study can be further increased by the dual ABE and CBE that can mediate both conversions (A-to-G and C-to-T) simultaneously, and also by prime editing approach which can widen the range of precise conversions in the desired region (Anzalone et al., 2019; Zhang et al., 2020).

The translational potential of genome edited HSPCs depends on long-term engraftment and repopulation ability. However, genotoxicity and cytotoxicity that can arise as a result of DSBs generated by programmable nucleases can be a limiting factor (Cullot et al., 2019; Yu et al., 2016). A previous study in nonhuman primates observed that the HBG promoter editing by Cas9 resulted in HBG2 deletion with up to 27% frequency and that cells with this deletion were under-represented after engraftment (Humbert et al., 2019). Base editing at the target sites of HBG1 and HBG2 promoter by ABE and CBE does not result in high frequency of large deletions in the intergenic region as seen with Cas9 and only showed low levels of indel formation. ABEs have an inherent advantage over CBEs as they generate desired edits (A:T to G:C) with high fidelity, whereas the latter generate unanticipated edits. In corroboration with existing findings, our results also suggest that ABE is a better base editor than CBE with respect to purity of base conversion and indel formation (Lee et al., 2018). Moreover, preliminary results from our study suggest that the base editing of the HSPCs by ABE8e variant with the novel site (by gRNA-11) elevated HbF to therapeutic levels in erythroid progeny. Further, our study did not observe any significant DNA and RNA off-target in the ABE and CBE edited cells. Our proof of principle study validated the various gRNAs that can elevate the HbF levels to therapeutic levels laying the groundwork for potential clinical applications. This approach could address a range of beta-globin disorders avoiding the need to develop specific therapeutic products for each of them.

In summary, we have demonstrated that CRISPR base editing can be utilized to drive the expression of HbF to therapeutically relevant levels in an erythroid progenitor cell line and in HSPCs. After screening every gRNAs within the 320 bp region of the HBG promoter, we identified nine gRNAs that, when paired with the appropriate base editor, can introduce HPFH-like mutations without the generation of indels. We identified five novel regulatory regions for HBG1 and HBG2 that are required for the silencing of gamma-globin in adult erythroid cells shedding light on the molecular mechanisms behind hemoglobin switching (Figure 7). Our work is an exemplification of base editors in mapping gene regulatory elements in highly homologous locus and we hope base editing strategy will be among the pre-eminent therapeutic strategies for monogenetic disorders like beta-hemoglobinopathies in the future.

Figure 7

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The transcriptome data have been deposited in GEO under accession code GSE192801 All the raw data from this study have been deposited in Dyrad (https://doi.org/10.5061/dryad.bzkh1897h).

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Author details

1. Nithin Sam Ravi

   1. Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India
   2. Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8063-6935

2. Beeke Wienert

   1. Innovative Genomics Institute, University of California, Berkeley, Berkeley, United States
   2. Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, United States
   3. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia

   Contribution

   Resources, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

3. Stacia K Wyman

   Innovative Genomics Institute, University of California, Berkeley, Berkeley, United States

   Contribution

   Methodology, Resources, Software

   Competing interests

   No competing interests declared

4. Henry William Bell

   School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia

   Contribution

   Methodology, Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4677-8208

5. Anila George

   1. Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India
   2. Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India

   Contribution

   Formal analysis, Writing – review and editing, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6016-976X

6. Gokulnath Mahalingam

   Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India

   Contribution

   Methodology

   Competing interests

   No competing interests declared

7. Jonathan T Vu

   Innovative Genomics Institute, University of California, Berkeley, Berkeley, United States

   Contribution

   Methodology, Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4950-7967

8. Kirti Prasad

   1. Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India
   2. Manipal Academy of Higher Education, Karnataka, India

   Contribution

   Methodology

   Competing interests

   No competing interests declared

9. Bhanu Prasad Bandlamudi

   Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India

   Contribution

   Methodology

   Competing interests

   No competing interests declared

10. Nivedhitha Devaraju

    1. Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India
    2. Manipal Academy of Higher Education, Karnataka, India

    Contribution

    Methodology

    Competing interests

    No competing interests declared

11. Vignesh Rajendiran

    1. Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India
    2. Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India

    Contribution

    Methodology

    Competing interests

    No competing interests declared

12. Nazar Syedbasha

    Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India

    Contribution

    Methodology

    Competing interests

    No competing interests declared

13. Aswin Anand Pai

    1. Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
    2. Department of Haematology, Christian Medical College & Hospital, Vellore, India

    Contribution

    Methodology, Resources

    Competing interests

    No competing interests declared

14. Yukio Nakamura

    Cell Engineering Division, RIKEN BioResource Center, Ibaraki, Japan

    Contribution

    Resources

    Competing interests

    No competing interests declared

15. Ryo Kurita

    Research and Development Department, Central Blood Institute Blood Service Headquarters, Japanese Red Cross Society, Japan, Tokyo, Japan

    Contribution

    Resources

    Competing interests

    No competing interests declared

16. Muthuraman Narayanasamy

    1. Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India
    2. Department of Biochemistry, Christian Medical College, Vellore, India

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

17. Poonkuzhali Balasubramanian

    1. Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
    2. Department of Haematology, Christian Medical College & Hospital, Vellore, India

    Contribution

    Methodology, Resources

    Competing interests

    No competing interests declared

18. Saravanabhavan Thangavel

    Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

19. Srujan Marepally

    Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

20. Shaji R Velayudhan

    1. Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India
    2. Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
    3. Department of Haematology, Christian Medical College & Hospital, Vellore, India

    Contribution

    Methodology, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

21. Alok Srivastava

    1. Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India
    2. Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
    3. Department of Haematology, Christian Medical College & Hospital, Vellore, India

    Contribution

    Project administration, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

22. Mark A DeWitt

    1. Innovative Genomics Institute, University of California, Berkeley, Berkeley, United States
    2. Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, United States

    Contribution

    Methodology, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

23. Merlin Crossley

    School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia

    Contribution

    Investigation, Methodology, Resources, Visualization, Writing – review and editing

    Competing interests

    No competing interests declared

24. Jacob E Corn

    1. Innovative Genomics Institute, University of California, Berkeley, Berkeley, United States
    2. Institute of Molecular Health Sciences, Department of Biology, Zurich, Switzerland

    Contribution

    Methodology, Resources, Visualization, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7798-5309

25. Kumarasamypet M Mohankumar

    1. Centre for Stem Cell Research (a Unit of inStem, Bengaluru), Christian Medical College Campus, Vellore, India
    2. Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing

    For correspondence

    mohankumarkm@cmcvellore.ac.in

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9407-1800

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