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.

Figure 1 with 1 supplement see all

Download asset Open asset

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.

Figure 2 with 3 supplements see all

Download asset Open asset

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).

Figure 3 with 4 supplements see all

Download asset Open asset

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.

Figure 4

Download asset Open asset

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.

Figure 5 with 1 supplement see all

Download asset Open asset

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.

https://cdn.elifesciences.org/articles/65421/elife-65421-fig5-data1-v2.zip

Download elife-65421-fig5-data1-v2.zip

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

Download asset Open asset

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

Download asset Open asset

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).

1. 1. Ravi N
   2. Wyman SK
   3. Mohankumar KM

   (2022) NCBI Gene Expression Omnibus

   ID GSE192801. Identification of novel HPFH-like mutations by CRISPR base editing that elevate the expression of fetal hemoglobin.

   http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE192801
2. 1. Mohankumar KM

   (2022) Dryad Digital Repository

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

   https://doi.org/10.5061/dryad.bzkh1897h

1. 1. Andrews NC
   2. Faller DV

   (1991) A rapid micropreparation technique for extraction of DNA-binding proteins from limiting numbers of mammalian cells

   Nucleic Acids Research 19:2499.

   https://doi.org/10.1093/nar/19.9.2499

   • PubMed
   • Google Scholar
2. 1. Anzalone AV
   2. Randolph PB
   3. Davis JR
   4. Sousa AA
   5. Koblan LW
   6. Levy JM
   7. Chen PJ
   8. Wilson C
   9. Newby GA
   10. Raguram A
   11. Liu DR

   (2019) Search-and-replace genome editing without double-strand breaks or donor DNA

   Nature 576:149–157.

   https://doi.org/10.1038/s41586-019-1711-4

   • PubMed
   • Google Scholar
3. 1. Anzalone AV
   2. Koblan LW
   3. Liu DR

   (2020) Genome editing with CRISPR-Cas nucleases, base editors, transposases and prime editors

   Nature Biotechnology 38:824–844.

   https://doi.org/10.1038/s41587-020-0561-9

   • PubMed
   • Google Scholar
4. 1. Arbab M
   2. Shen MW
   3. Mok B
   4. Wilson C
   5. Matuszek Ż
   6. Cassa CA
   7. Liu DR

   (2020) Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning

   Cell 182:463–480.

   https://doi.org/10.1016/j.cell.2020.05.037

   • PubMed
   • Google Scholar
5. 1. Barczak W
   2. Suchorska W
   3. Rubiś B
   4. Kulcenty K

   (2015) Universal real-time PCR-based assay for lentiviral titration

   Molecular Biotechnology 57:195–200.

   https://doi.org/10.1007/s12033-014-9815-4

   • PubMed
   • Google Scholar
6. 1. Bauer DE
   2. Orkin SH

   (2012) Update on fetal hemoglobin gene regulation in hemoglobinopathies

   Current Opinion in Pediatrics 23:617–632.

   https://doi.org/10.1097/MOP.0b013e3283420fd0.Update

   • Google Scholar
7. 1. Canver MC
   2. Smith EC
   3. Sher F
   4. Pinello L
   5. Sanjana NE
   6. Shalem O
   7. Chen DD
   8. Schupp PG
   9. Vinjamur DS
   10. Garcia SP
   11. Luc S
   12. Kurita R
   13. Nakamura Y
   14. Fujiwara Y
   15. Maeda T
   16. Yuan GC
   17. Zhang F
   18. Orkin SH
   19. Bauer DE

   (2015) BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis

   Nature 527:192–197.

   https://doi.org/10.1038/nature15521

   • PubMed
   • Google Scholar
8. 1. Cavazzana M
   2. Antoniani C
   3. Miccio A

   (2017) Gene Therapy for β-Hemoglobinopathies

   Molecular Therapy 25:1142–1154.

   https://doi.org/10.1016/j.ymthe.2017.03.024

   • PubMed
   • Google Scholar
9. 1. Choi K
   2. Ratner N

   (2019) iGEAK: an interactive gene expression analysis kit for seamless workflow using the R/shiny platform

   BMC Genomics 20:1–7.

   https://doi.org/10.1186/s12864-019-5548-x

   • PubMed
   • Google Scholar
10. 1. Clement K
    2. Rees H
    3. Canver MC
    4. Gehrke JM
    5. Farouni R
    6. Hsu JY
    7. Cole MA
    8. Liu DR
    9. Joung JK
    10. Bauer DE
    11. Pinello L

    (2019) CRISPResso2 provides accurate and rapid genome editing sequence analysis

    Nature Biotechnology 37:224–226.

    https://doi.org/10.1038/s41587-019-0032-3

    • PubMed
    • Google Scholar
11. Software

    1. Corn JE

    (2017) Preparation of PCR amplicons from edited cells for deep sequencing

    Protocols.

    https://www.protocols.io/view/preparation-of-pcr-amplicons-from-edited-cells-for-6ruhd6w
12. 1. Crossley M
    2. Whitelaw E
    3. Perkins A
    4. Williams G
    5. Fujiwara Y
    6. Orkin SH

    (1996) Isolation and characterization of the cDNA encoding BKLF/TEF-2, a major CACCC-box-binding protein in erythroid cells and selected other cells

    Molecular and Cellular Biology 16:1695–1705.

    https://doi.org/10.1128/MCB.16.4.1695

    • PubMed
    • Google Scholar
13. 1. Cullot G
    2. Boutin J
    3. Toutain J
    4. Prat F
    5. Pennamen P
    6. Rooryck C
    7. Teichmann M
    8. Rousseau E
    9. Lamrissi-Garcia I
    10. Guyonnet-Duperat V
    11. Bibeyran A
    12. Lalanne M
    13. Prouzet-Mauléon V
    14. Turcq B
    15. Ged C
    16. Blouin JM
    17. Richard E
    18. Dabernat S
    19. Moreau-Gaudry F
    20. Bedel A

    (2019) CRISPR-Cas9 genome editing induces megabase-scale chromosomal truncations

    Nature Communications 10:1136.

    https://doi.org/10.1038/s41467-019-09006-2

    • PubMed
    • Google Scholar
14. 1. Ewels PA
    2. Peltzer A
    3. Fillinger S
    4. Patel H
    5. Alneberg J
    6. Wilm A
    7. Garcia MU
    8. Di Tommaso P
    9. Nahnsen S

    (2020) The nf-core framework for community-curated bioinformatics pipelines

    Nature Biotechnology 38:276–278.

    https://doi.org/10.1038/s41587-020-0439-x

    • Google Scholar
15. 1. Fischer KD
    2. Nowock J

    (1990) The T----C substitution at -198 of the A gamma-globin gene associated with the British form of HPFH generates overlapping recognition sites for two DNA-binding proteins

    Nucleic Acids Research 18:5685–5693.

    https://doi.org/10.1093/nar/18.19.5685

    • PubMed
    • Google Scholar
16. 1. Fucharoen S
    2. Shimizu K
    3. Fukumaki Y

    (1990) A novel C-T transition within the distal CCAAT motif of the G gamma-globin gene in the Japanese HPFH: implication of factor binding in elevated fetal globin expression

    Nucleic Acids Research 18:5245–5253.

    https://doi.org/10.1093/nar/18.17.5245

    • PubMed
    • Google Scholar
17. 1. Gaudelli NM
    2. Komor AC
    3. Rees HA
    4. Packer MS
    5. Badran AH
    6. Bryson DI
    7. Liu DR

    (2017) Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage

    Nature 551:464–471.

    https://doi.org/10.1038/nature24644

    • PubMed
    • Google Scholar
18. 1. Genovese P
    2. Schiroli G
    3. Escobar G
    4. Tomaso TD
    5. Firrito C
    6. Calabria A
    7. Moi D
    8. Mazzieri R
    9. Bonini C
    10. Holmes MC
    11. Gregory PD
    12. van der Burg M
    13. Gentner B
    14. Montini E
    15. Lombardo A
    16. Naldini L

    (2014) Targeted genome editing in human repopulating haematopoietic stem cells

    Nature 510:235–240.

    https://doi.org/10.1038/nature13420

    • PubMed
    • Google Scholar
19. 1. Gilman JG
    2. Mishima N
    3. Wen XJ
    4. Stoming TA
    5. Lobel J
    6. Huisman TH

    (1988) Distal CCAAT box deletion in the A gamma globin gene of two black adolescents with elevated fetal A gamma globin

    Nucleic Acids Research 16:10635–10642.

    https://doi.org/10.1093/nar/16.22.10635

    • PubMed
    • Google Scholar
20. Software

    1. Giudice CL

    (2022) REDItools2

    GitHub.

    https://github.com/BioinfoUNIBA/REDItools2
21. 1. Gluzman Y

    (1981) SV40-transformed simian cells support the replication of early SV40 mutants

    Cell 23:175–182.

    https://doi.org/10.1016/0092-8674(81)90282-8

    • PubMed
    • Google Scholar
22. 1. Heckl D
    2. Kowalczyk MS
    3. Yudovich D
    4. Belizaire R
    5. Puram RV
    6. McConkey ME
    7. Thielke A
    8. Aster JC
    9. Regev A
    10. Ebert BL

    (2014) Generation of mouse models of myeloid malignancy with combinatorial genetic lesions using CRISPR-Cas9 genome editing

    Nature Biotechnology 32:941–946.

    https://doi.org/10.1038/nbt.2951

    • PubMed
    • Google Scholar
23. Preprint

    1. Hsiau T
    2. Conant D
    3. Rossi N
    4. Maures T
    5. Waite K
    6. Yang J
    7. Joshi S
    8. Kelso R
    9. Holden K
    10. Enzmann BL
    11. Stoner R

    (2018) Inference of CRISPR Edits from Sanger Trace Data

    bioRxiv.

    https://doi.org/10.1101/251082

    • Google Scholar
24. 1. Humbert O
    2. Radtke S
    3. Samuelson C
    4. Carrillo RR
    5. Perez AM
    6. Reddy SS
    7. Lux C
    8. Pattabhi S
    9. Schefter LE
    10. Negre O
    11. Lee CM
    12. Bao G
    13. Adair JE
    14. Peterson CW
    15. Rawlings DJ
    16. Scharenberg AM
    17. Kiem H-P

    (2019) Therapeutically relevant engraftment of a CRISPR-Cas9-edited HSC-enriched population with HbF reactivation in nonhuman primates

    Science Translational Medicine 11:1–14.

    https://doi.org/10.1126/scitranslmed.aaw3768

    • PubMed
    • Google Scholar
25. 1. Ikuta T
    2. Papayannopoulou T
    3. Stamatoyannopoulos G
    4. Kan YW

    (1996) Globin Gene Switching

    Journal of Biological Chemistry 271:14082–14091.

    https://doi.org/10.1074/jbc.271.24.14082

    • PubMed
    • Google Scholar
26. 1. Jacob GF
    2. Raper AB

    (1958) Hereditary persistence of foetal haemoglobin production, and its interaction with the sickle-cell trait

    British Journal of Haematology 4:138–149.

    https://doi.org/10.1111/j.1365-2141.1958.tb03844.x

    • PubMed
    • Google Scholar
27. 1. Kluesner MG
    2. Nedveck DA
    3. Lahr WS
    4. Garbe JR
    5. Abrahante JE
    6. Webber BR
    7. Moriarity BS

    (2018) EditR: A Method to Quantify Base Editing from Sanger Sequencing

    The CRISPR Journal 1:239–250.

    https://doi.org/10.1089/crispr.2018.0014

    • PubMed
    • Google Scholar
28. 1. Koblan LW
    2. Doman JL
    3. Wilson C
    4. Levy JM
    5. Tay T
    6. Newby GA
    7. Maianti JP
    8. Raguram A
    9. Liu DR

    (2018) Improving cytidine and adenine base editors by expression optimization and ancestral reconstruction

    Nature Biotechnology 36:843–846.

    https://doi.org/10.1038/nbt.4172

    • PubMed
    • Google Scholar
29. 1. Koblan LW
    2. Erdos MR
    3. Wilson C
    4. Cabral WA
    5. Levy JM
    6. Xiong ZM
    7. Tavarez UL
    8. Davison LM
    9. Gete YG
    10. Mao X
    11. Newby GA
    12. Doherty SP
    13. Narisu N
    14. Sheng Q
    15. Krilow C
    16. Lin CY
    17. Gordon LB
    18. Cao K
    19. Collins FS
    20. Brown JD
    21. Liu DR

    (2021) In vivo base editing rescues Hutchinson-Gilford progeria syndrome in mice

    Nature 589:608–614.

    https://doi.org/10.1038/s41586-020-03086-7

    • PubMed
    • Google Scholar
30. 1. Komor AC
    2. Kim YB
    3. Packer MS
    4. Zuris JA
    5. Liu DR

    (2016) Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage

    Nature 533:420–424.

    https://doi.org/10.1038/nature17946

    • PubMed
    • Google Scholar
31. 1. Kurita R
    2. Suda N
    3. Sudo K
    4. Miharada K
    5. Hiroyama T
    6. Miyoshi H
    7. Tani K
    8. Nakamura Y

    (2013) Establishment of Immortalized Human Erythroid Progenitor Cell Lines Able to Produce Enucleated Red Blood Cells

    PLOS ONE 8:e0059890.

    https://doi.org/10.1371/journal.pone.0059890

    • Google Scholar
32. 1. Kuzminov A

    (2001) Single-strand interruptions in replicating chromosomes cause double-strand breaks

    PNAS 98:8241–8246.

    https://doi.org/10.1073/pnas.131009198

    • PubMed
    • Google Scholar
33. 1. Langmead B
    2. Salzberg S

    (2013) Fast gapped-read alignment with Bowtie 2 Ben

    Nature Methods 9:357–359.

    https://doi.org/10.1038/nmeth.1923.Fast

    • Google Scholar
34. 1. Lee HK
    2. Willi M
    3. Miller SM
    4. Kim S
    5. Liu C
    6. Liu DR
    7. Hennighausen L

    (2018) Targeting fidelity of adenine and cytosine base editors in mouse embryos

    Nature Communications 9:7–12.

    https://doi.org/10.1038/s41467-018-07322-7

    • PubMed
    • Google Scholar
35. 1. Li C
    2. Psatha N
    3. Sova P
    4. Gil S
    5. Wang H
    6. Kim J
    7. Kulkarni C
    8. Valensisi C
    9. Hawkins RD
    10. Stamatoyannopoulos G
    11. Lieber A

    (2018) Reactivation of γ-globin in adult β-YAC mice after ex vivo and in vivo hematopoietic stem cell genome editing

    Blood 131:2915–2928.

    https://doi.org/10.1182/blood-2018-03-838540

    • PubMed
    • Google Scholar
36. 1. Liu N
    2. Zhu Q
    3. Xu J
    4. Bulyk ML
    5. Orkin SH
    6. Liu N
    7. Zhu Q
    8. Hong J
    9. Kim W
    10. Sher F

    (2018) Direct Promoter Repression by BCL11A Controls the Fetal to Adult Hemoglobin Switch Article Direct Promoter Repression by BCL11A Controls the Fetal to Adult Hemoglobin Switch

    Cell 173:1–13.

    https://doi.org/10.1016/j.cell.2018.03.016

    • Google Scholar
37. 1. Loucari CC
    2. Patsali P
    3. van Dijk TB
    4. Stephanou C
    5. Papasavva P
    6. Zanti M
    7. Kurita R
    8. Nakamura Y
    9. Christou S
    10. Sitarou M
    11. Philipsen S
    12. Lederer CW
    13. Kleanthous M

    (2018) Rapid and Sensitive Assessment of Globin Chains for Gene and Cell Therapy of Hemoglobinopathies

    Human Gene Therapy Methods 29:60–74.

    https://doi.org/10.1089/hgtb.2017.190

    • PubMed
    • Google Scholar
38. 1. Mahalingam G
    2. Mohan A
    3. Arjunan P
    4. Dhyani AK

    (2022) Using Lipid Nanoparticles for the Delivery of Chemically Modified mRNA into Mammalian Cells

    JoVE 2:e62407.

    https://doi.org/10.3791/62407

    • Google Scholar
39. 1. Martyn GE
    2. Wienert B
    3. Yang L
    4. Shah M
    5. Norton LJ
    6. Burdach J
    7. Kurita R
    8. Nakamura Y
    9. Pearson RCM
    10. Funnell APW
    11. Quinlan KGR
    12. Crossley M

    (2018) Natural regulatory mutations elevate the fetal globin gene via disruption of BCL11A or ZBTB7A binding

    Nature Genetics 50:498–503.

    https://doi.org/10.1038/s41588-018-0085-0

    • PubMed
    • Google Scholar
40. 1. Martyn GE
    2. Wienert B
    3. Kurita R
    4. Nakamura Y
    5. Quinlan KGR
    6. Crossley M

    (2019) A natural regulatory mutation in the proximal promoter elevates fetal globin expression by creating a de novo GATA1 site

    Blood 133:852–856.

    https://doi.org/10.1182/blood-2018-07-863951

    • PubMed
    • Google Scholar
41. 1. Métais J-Y
    2. Doerfler PA
    3. Mayuranathan T
    4. Bauer DE
    5. Fowler SC
    6. Hsieh MM
    7. Katta V
    8. Keriwala S
    9. Lazzarotto CR
    10. Luk K
    11. Neel MD
    12. Perry SS
    13. Peters ST
    14. Porter SN
    15. Ryu BY
    16. Sharma A
    17. Shea D
    18. Tisdale JF
    19. Uchida N
    20. Wolfe SA
    21. Woodard KJ
    22. Wu Y
    23. Yao Y
    24. Zeng J
    25. Pruett-Miller S
    26. Tsai SQ
    27. Weiss MJ

    (2019) Genome editing of HBG1 and HBG2 to induce fetal hemoglobin

    Blood Advances 3:3379–3392.

    https://doi.org/10.1182/bloodadvances.2019000820

    • PubMed
    • Google Scholar
42. 1. Miller IJ
    2. Bieker JJ

    (1993) A novel, erythroid cell-specific murine transcription factor that binds to the CACCC element and is related to the Krüppel family of nuclear proteins

    Molecular and Cellular Biology 13:2776–2786.

    https://doi.org/10.1128/mcb.13.5.2776-2786.1993

    • PubMed
    • Google Scholar
43. 1. Motum PI
    2. Deng ZM
    3. Huong L
    4. Trent RJ

    (1994) The Australian type of nondeletional G gamma-HPFH has a C-->G substitution at nucleotide -114 of the G gamma gene

    British Journal of Haematology 86:219–221.

    https://doi.org/10.1111/j.1365-2141.1994.tb03284.x

    • PubMed
    • Google Scholar
44. 1. Psatha N
    2. Reik A
    3. Phelps S
    4. Zhou Y
    5. Dalas D
    6. Yannaki E
    7. Levasseur DN
    8. Urnov FD
    9. Holmes MC
    10. Papayannopoulou T

    (2018) Disruption of the BCL11A Erythroid Enhancer Reactivates Fetal Hemoglobin in Erythroid Cells of Patients with β-Thalassemia Major

    Molecular Therapy. Methods & Clinical Development 10:313–326.

    https://doi.org/10.1016/j.omtm.2018.08.003

    • PubMed
    • Google Scholar
45. 1. Ran FA
    2. Hsu PD
    3. Lin CY
    4. Gootenberg JS
    5. Konermann S
    6. Trevino AE
    7. Scott DA
    8. Inoue A
    9. Matoba S
    10. Zhang Y
    11. Zhang F

    (2013a) Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity

    Cell 154:1380–1389.

    https://doi.org/10.1016/j.cell.2013.08.021

    • PubMed
    • Google Scholar
46. 1. Ran FA
    2. Hsu PD
    3. Wright J
    4. Agarwala V
    5. Scott DA
    6. Zhang F

    (2013b) Genome engineering using the CRISPR-Cas9 system

    Nature Protocols 8:2281–2308.

    https://doi.org/10.1038/nprot.2013.143

    • PubMed
    • Google Scholar
47. 1. Richter MF
    2. Zhao KT
    3. Eton E
    4. Lapinaite A
    5. Newby GA
    6. Thuronyi BW
    7. Wilson C
    8. Koblan LW
    9. Zeng J
    10. Bauer DE
    11. Doudna JA
    12. Liu DR

    (2020) Phage-assisted evolution of an adenine base editor with improved Cas domain compatibility and activity

    Nature Biotechnology 38:883–891.

    https://doi.org/10.1038/s41587-020-0453-z

    • PubMed
    • Google Scholar
48. 1. Robinson JT

    (2012) Integrative Genomics Viewer

    Nature Biotechnology 29:24–26.

    https://doi.org/10.1038/nbt.1754.Integrative

    • Google Scholar
49. 1. Sambrook J
    2. Russell DW

    (2006) Preparation and Transformation of Competent E. coli Using Calcium Chloride

    CSH Protocols 2006:pdb.prot3932.

    https://doi.org/10.1101/pdb.prot3932

    • PubMed
    • Google Scholar
50. 1. Sanjana NE
    2. Shalem O
    3. Zhang F

    (2014) Improved vectors and genome-wide libraries for CRISPR screening HHS Public Access Supplementary Material

    Nature Methods 11:783–784.

    https://doi.org/10.1038/nmeth.3047.Improved

    • Google Scholar
51. 1. Schiml S
    2. Fauser F
    3. Puchta H

    (2016) Repair of adjacent single-strand breaks is often accompanied by the formation of tandem sequence duplications in plant genomes

    PNAS 113:7266–7271.

    https://doi.org/10.1073/pnas.1603823113

    • PubMed
    • Google Scholar
52. 1. Shalem O
    2. Sanjana NE
    3. Hartenian E
    4. Shi X
    5. Scott DA
    6. Mikkelson T
    7. Heckl D
    8. Ebert BL
    9. Root DE
    10. Doench JG
    11. Zhang F

    (2014) Genome-scale CRISPR-Cas9 knockout screening in human cells

    Science (New York, N.Y.) 343:84–87.

    https://doi.org/10.1126/science.1247005

    • PubMed
    • Google Scholar
53. 1. Shariati SA
    2. Dominguez A
    3. Xie S
    4. Wernig M
    5. Qi LS
    6. Skotheim JM

    (2019) Reversible Disruption of Specific Transcription Factor-DNA Interactions Using CRISPR/Cas9

    Molecular Cell 74:622–633.

    https://doi.org/10.1016/j.molcel.2019.04.011

    • PubMed
    • Google Scholar
54. 1. Stoming T
    2. Stoming G
    3. Lanclos K
    4. Fei Y
    5. Altay C
    6. Kutlar F
    7. Huisman T

    (1989) An A gamma type of nondeletional hereditary persistence of fetal hemoglobin with a T----C mutation at position -175 to the cap site of the A gamma globin gene

    Blood 73:329–333.

    https://doi.org/10.1182/blood.V73.1.329.329

    • Google Scholar
55. 1. Tallack MR
    2. Whitington T
    3. Yuen WS
    4. Wainwright EN
    5. Keys JR
    6. Gardiner BB
    7. Nourbakhsh E
    8. Cloonan N
    9. Grimmond SM
    10. Bailey TL
    11. Perkins AC

    (2010) A global role for KLF1 in erythropoiesis revealed by ChIP-seq in primary erythroid cells

    Genome Research 20:1052–1063.

    https://doi.org/10.1101/gr.106575.110

    • PubMed
    • Google Scholar
56. 1. Tate VE
    2. Wood WG
    3. Weatherall DJ

    (1986) The British form of hereditary persistence of fetal hemoglobin results from a single base mutation adjacent to an S1 hypersensitive site 5’ to the A gamma globin gene

    Blood 68:1389–1393.

    https://doi.org/10.1182/blood.V68.6.1389.bloodjournal6861389

    • PubMed
    • Google Scholar
57. 1. Thein SL

    (2018) Molecular basis of β thalassemia and potential therapeutic targets

    Blood Cells, Molecules & Diseases 70:54–65.

    https://doi.org/10.1016/j.bcmd.2017.06.001

    • PubMed
    • Google Scholar
58. 1. Trakarnsanga K
    2. Griffiths RE
    3. Wilson MC
    4. Blair A
    5. Satchwell TJ
    6. Meinders M
    7. Cogan N
    8. Kupzig S
    9. Kurita R
    10. Nakamura Y
    11. Toye AM
    12. Anstee DJ
    13. Frayne J

    (2017) An immortalized adult human erythroid line facilitates sustainable and scalable generation of functional red cells

    Nature Communications 8:14750.

    https://doi.org/10.1038/ncomms14750

    • PubMed
    • Google Scholar
59. 1. Traxler EA
    2. Yao Y
    3. Wang Y-D
    4. Woodard KJ
    5. Kurita R
    6. Nakamura Y
    7. Hughes JR
    8. Hardison RC
    9. Blobel GA
    10. Li C
    11. Weiss MJ

    (2016) A genome-editing strategy to treat β-hemoglobinopathies that recapitulates a mutation associated with a benign genetic condition

    Nature Medicine 22:987–990.

    https://doi.org/10.1038/nm.4170

    • PubMed
    • Google Scholar
60. 1. Webber BR
    2. Lonetree C-L
    3. Kluesner MG
    4. Johnson MJ
    5. Pomeroy EJ
    6. Diers MD
    7. Lahr WS
    8. Draper GM
    9. Slipek NJ
    10. Smeester BA
    11. Lovendahl KN
    12. McElroy AN
    13. Gordon WR
    14. Osborn MJ
    15. Moriarity BS

    (2019) Highly efficient multiplex human T cell engineering without double-strand breaks using Cas9 base editors

    Nature Communications 10:5222.

    https://doi.org/10.1038/s41467-019-13007-6

    • PubMed
    • Google Scholar
61. 1. Wienert B
    2. Funnell APW
    3. Norton LJ
    4. Pearson RCM
    5. Wilkinson-White LE
    6. Lester K
    7. Vadolas J
    8. Porteus MH
    9. Matthews JM
    10. Quinlan KGR
    11. Crossley M

    (2015) Editing the genome to introduce a beneficial naturally occurring mutation associated with increased fetal globin

    Nature Communications 6:1–8.

    https://doi.org/10.1038/ncomms8085

    • PubMed
    • Google Scholar
62. 1. Wienert B
    2. Martyn GE
    3. Kurita R
    4. Nakamura Y
    5. Quinlan KGR
    6. Crossley M

    (2017) KLF1 drives the expression of fetal hemoglobin in British HPFH

    Blood 130:803–807.

    https://doi.org/10.1182/blood-2017-02-767400

    • PubMed
    • Google Scholar
63. 1. Wienert B
    2. Martyn GE
    3. Funnell APW
    4. Quinlan KGR
    5. Crossley M

    (2018) Wake-up Sleepy Gene: Reactivating Fetal Globin for β-Hemoglobinopathies

    Trends in Genetics 34:927–940.

    https://doi.org/10.1016/j.tig.2018.09.004

    • PubMed
    • Google Scholar
64. 1. Wimberly H
    2. Shee C
    3. Thornton PC
    4. Sivaramakrishnan P
    5. Rosenberg SM
    6. Hastings PJ

    (2013) R-loops and nicks initiate DNA breakage and genome instability in non-growing Escherichia coli

    Nature Communications 4:1–10.

    https://doi.org/10.1038/ncomms3115

    • PubMed
    • Google Scholar
65. 1. Yang Y
    2. Xu Z
    3. He C
    4. Zhang B
    5. Shi Y
    6. Li F

    (2019) Structural insights into the recognition of γ-globin gene promoter by BCL11A

    Cell Research 29:960–963.

    https://doi.org/10.1038/s41422-019-0221-0

    • PubMed
    • Google Scholar
66. 1. Yu K-R
    2. Corat MAF
    3. Metais J-Y
    4. Dunbar CE

    (2016) 564. The Cytotoxic Effect of RNA-Guided Endonuclease Cas9 on Human Hematopoietic Stem and Progenitor Cells (HSPCs)

    Molecular Therapy 24:S225–S226.

    https://doi.org/10.1016/S1525-0016(16)33372-X

    • Google Scholar
67. 1. Zafra MP
    2. Schatoff EM
    3. Katti A
    4. Foronda M
    5. Breinig M
    6. Schweitzer AY
    7. Simon A
    8. Han T
    9. Goswami S
    10. Montgomery E
    11. Thibado J
    12. Kastenhuber ER
    13. Sánchez-Rivera FJ
    14. Shi J
    15. Vakoc CR
    16. Lowe SW
    17. Tschaharganeh DF
    18. Dow LE

    (2018) Optimized base editors enable efficient editing in cells, organoids and mice

    Nature Biotechnology 36:888–893.

    https://doi.org/10.1038/nbt.4194

    • PubMed
    • Google Scholar
68. 1. Zertal-Zidani S
    2. Merghoub T
    3. Ducrocq R
    4. Gerard N
    5. Satta D
    6. Krishnamoorthy R

    (1999) A novel C-->A transversion within the distal CCAAT motif of the Ggamma-globin gene in the Algerian Ggammabeta+-hereditary persistence of fetal hemoglobin

    Hemoglobin 23:159–169.

    https://doi.org/10.3109/03630269908996160

    • PubMed
    • Google Scholar
69. 1. Zhang X
    2. Zhu B
    3. Chen L
    4. Xie L
    5. Yu W
    6. Wang Y
    7. Li L
    8. Yin S
    9. Yang L
    10. Hu H
    11. Han H
    12. Li Y
    13. Wang L
    14. Chen G
    15. Ma X
    16. Geng H
    17. Huang W
    18. Pang X
    19. Yang Z
    20. Wu Y
    21. Siwko S
    22. Kurita R
    23. Nakamura Y
    24. Yang L
    25. Liu M
    26. Li D

    (2020) Dual base editor catalyzes both cytosine and adenine base conversions in human cells

    Nature Biotechnology 38:856–860.

    https://doi.org/10.1038/s41587-020-0527-y

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for sending your article entitled "Identification of novel HPFH-like mutations by CRISPR base editing that elevate the expression of fetal hemoglobin" for peer review at eLife. Your article is being evaluated by 2 peer reviewers, and the evaluation is being overseen by a Reviewing Editor and Patricia Wittkopp as the Senior Editor.

The main concerns are the extent and varied nature of the different technical aspects of the paper. Whether these can be addressed in a focused fashion within a reasonable time-period is the question at hand.

Reviewer #1:

Disrupting transcriptional regulation of HBG to induce fetal hemoglobin (HbF) expression is a promising therapeutic strategy for sickle cell disease (SCD). To identify novel HBG regulatory elements, Ravi et al., utilized cytosine (CBE) and adenine (ABE) base editors to mutagenize the proximal HBG promoter region in HUDEP-2 immortalized human erythroid progenitor cells. The authors were able to achieve successful editing across a number of target sites with several inducing functional upregulation of HbF. Notably, for one ABE target the induction of HBG could be explained by the creation of a consensus binding site for KLF1, although the degree of induction was less than that achieved by disruption of a previously identified BCL11A binding site. These data highlight advantages of using base editors as a mutagenesis tool and extend our current understanding of HBG gene regulation which may have future therapeutic relevance in SCD.

Strengths:

Genetic mapping of key regulatory elements within the HGB promoter region has been conducted using nuclease-based mutagenesis but is limited by the imprecise repair outcomes of NHEJ and by frequent deletion of the region intervening the highly homologous sequences of the duplicated HBG1 and HBG2 loci. The use of base editors is an elegant strategy to overcome these challenges.

Although nuclease-based indel induction can be readily used to disrupt regulatory elements, it is less useful for the introduction of novel transcription factor binding sites unless paired with inefficient homology directed repair approaches. The current study demonstrates a key advantage of base editing which is the ability to efficiently create transcription factor binding sites. This broadens the scope of functional alterations that can be introduced within gene regulatory elements.

In addition to demonstrating that base editors are a useful tool for regulatory element mapping, the current study sheds new light on the regulation of HBG expression by resolving five additional regulatory regions located within the HBG proximal promoter region that could have therapeutic relevance in SCD.

Weaknesses:

Although the paper successfully identifies five novel regulatory regions within the HBG promoter that when modified increase HbF expression, none of these edits induce HbF as effectively as disruption of the previously identified BCL11A binding site. Thus, from a translational standpoint the results do not appear to be a significant advance. However, the substantial variation in editing at each site may be limiting the magnitude of HbF induction.

Variable editing across target sites has been described for both CBE and ABE, so it's not unexpected that it was observed in the current study. However, without data showing the transduction efficiency of the sgRNA vectors (by GFP flow) it is difficult to conclude that the variation is solely a function of the target or target sequence context. At a few of the sites with low genetic editing there is a significant increase in HbF expression, suggesting that if the editing at these sites were improved the functional induction of HbF could be substantial.

The use of lentivirus delivered gRNAs would appear to be amenable to pooled screening, but in the current study the screen was conducted in an arrayed fashion. The ability to conduct a pooled screen could substantially expand the utility of this approach and it would have been very interesting to see how the results from a pooled and arrayed screen compared to one another.

Lastly, while the functional readouts in the current study are compelling and largely correlate with the editing, confirmation in a second system would further inspire confidence in the results. In particular, members of this team and others have carried out editing in primary human CD34+ hematopoietic stem/progenitor cells (HSPC) followed by erythroid differentiation. Confirmation of the identified targets in primary cells with therapeutic relevance would inspire further confidence in the results.

– GFP flow data for the transduction efficiency of sgRNA vectors should be included. This is critical to confirm that the editing variability is due to target site and not transduction efficiency. It would also be helpful to confirm that none of the gRNA sequences contain premature termination sequences as well.

– The nature of the replicates are unclear. In figures 2 and 3 there are no error bars and in the figures with error bars it in not clear the definition of the biological replicates. Were these independent transductions of the cells, or repeat measures of the same transduced populations?

– In the functional validation experiments, were these independent transductions or use of the initial transduced populations? What were the sgRNA transduction rates in the populations used?

– Several previous editing strategies have been used to increase HbF. It would be informative if prior editing strategies were included in this study for comparison.

– Quantification of the EMSA in figure 6c should be included.

– Additional analysis to quantify and more clearly represent the correlation between genome editing and HBG (HbF) induction would be helpful for readers.

– Follow up validation of promising targets using transient transfection approaches would be informative as well.

– Despite low indels the frequency of deletion between HBG1 and 2 by PCR should be measured and the gel included next to a nuclease control.

– ABE gRNA 03 gave low genetic editing rates but apparently higher HbF induction. it would be very interesting to see how this target behaved if targeted with recently published hyperactive (ABE8e etc) variants. If high editing were achieved it would seem this could be an attractive target for therapy.

– Additional analysis to quantify and more clearly represent the correlation between genome editing and HBG (HbF) induction would be helpful for readers.

– For promising targets, confirmation in primary CD34+ HSPC would increase the impact of the current study.

– The observation of editing outside the spacer window described as novel in line 188 has been previously observed: Webber et al., Nature Comm (https://www.nature.com/articles/s41467-019-13007-6), Arbab et al., Cell (https://www.sciencedirect.com/science/article/abs/pii/S0092867420306322). These studies should be cited and text changed to describe these results as confirmatory.

– Line 221 describing reduction of double positive cells in CBE treated cells. Since results are not significant this statement should be removed.

Reviewer #2:

In this study, the Authors use base editors to generate mutations in the promoters of the genes encoding the γ-globin chains of the fetal hemoglobin in an adult erythroid cell line. Several of these mutations are associated with increased γ-globin gene expression, paving the way for the development of therapeutic strategies for β-hemoglobinopathy patients who have been shown to benefit from persistent expression fetal hemoglobin in adulthood. The major strengths of this work are: (i) the discovery of novel mutations associated with elevated feta hemoglobin levels; (ii) the impact of these discovery in the field of gene therapy for β-hemoglobinopathies.

The weakness of this study is the technical quality that can be improved to support the Authors' conclusions.

Remarks to the Authors:

– While many findings of this study are interesting, in the Results section it is not clear which are the specific questions. The results are a list of data that are partially interpreted only in the Discussion. I would suggest the Authors to better explain their findings in the Results section (e.g., comparison of different strategies disrupting repressor binding sites or recruiting transcriptional activators).

– It would be interesting to study if any of the mutations that do not increase HbF, down-regulate γ-globin expression (in cells that do express HbF) to identify binding sites for activators.

– The Authors should better characterize the cell lines expressing base editors. If I understood correctly, these cells are constitutively expressing the base editors (and the gRNAs). Some base editors are known to induce off-targets at DNA and RNA levels. This possibility should be investigated (at least the RNA off-targets) to verify that the major players in HbF regulation are properly expressed by performing a side-by-side comparison of cells expressing the base editors and untransduced cells. In addition, if base editors and gRNA are constitutively expressed, base editing efficiency can change overtime and should be evaluated in the same cells in which the Authors measure HbF expression.

– Evaluation of HbF+ cells (by flow cytometry) and globin mRNAs (by qRT-PCR) should be performed in differentiated cells, as HbF expression changes upon differentiation.

– The Authors should specify if control cells express the base editors and a control gRNA.

– The first screening of gRNAs is performed without replicates (Figure 2 and 3 and related supplementary figures). Furthermore, many of the gRNAs produce very few editing events, thus it is not possible rule out if the target regions are important for γ-globin expression. Therefore, I would exclude from the analysis the gRNAs associated with a low genome editing as these results are confounding and do not give insights in the regulation of γ-globin expression. Maybe Figure 2 and 3 (and related supplementary figures) could be kept as supplementary data.

– Line 178: the -117 mutation (occurring upstream of the gRNA target site) should be reported in Figure 4.

– Interestingly, the Authors observed that the -123/-124 mutations create a binding site for KLF1. However, most of the editing events generate only one of the 2 mutations (figure 4 suppl 2), would individual mutations be sufficient to generate a KLF1 binding site? Can this be tested by performing an EMSA assay with oligos harboring individual mutations? If individual mutations are not generating a KLF1 binding site, would the observed HbF reactivation be justified by the recruitment of KLF1 only to a minor fraction of the alleles harboring the concomitant editing of the 2 nucleotides?

Finally, can the Authors perform ChIP experiments to demonstrate the increase KLF1 recruitment upon editing of the -123/-124 region?

– Lines 311-314: "CBE mediated base conversion (C to T) at positions – 114 and -115 resulted in significantly greater induction of HbF than the multiple A to G nucleotide substitutions at -110, -112, -113, -116 positions made by ABE." I believe that this or similar statements should be supported by the analysis of clonal populations harboring the same number of edited promoters.

– Figure 3-Suppl Figure 1: it would be helpful to plot HbF expression of the negative controls.

– SEM is missing in Figure 4-suppl Figure 1, panels A and C.

– Figure 5-Suppl Figure 1, panel d: why α and β chains are reduced in samples gRNA02 and gRNA 42?

– Figure 5-Suppl Figure 1: Can the Authors comment on the imbalanced Agamma/Ggamma ratio in sample gRNA 42? Could it be due to the potential deletion of the HBG2 gene? Besides Indels, did the Authors measure the frequency of the 4.9kb deletions in base-edited samples?

– It would be interesting to perform experiments in primary cells to validate these interesting findings.

Reviewer #1:

Disrupting transcriptional regulation of HBG to induce fetal hemoglobin (HbF) expression is a promising therapeutic strategy for sickle cell disease (SCD). To identify novel HBG regulatory elements, Ravi et al., utilized cytosine (CBE) and adenine (ABE) base editors to mutagenize the proximal HBG promoter region in HUDEP-2 immortalized human erythroid progenitor cells. The authors were able to achieve successful editing across a number of target sites with several inducing functional upregulation of HbF. Notably, for one ABE target the induction of HBG could be explained by the creation of a consensus binding site for KLF1, although the degree of induction was less than that achieved by disruption of a previously identified BCL11A binding site. These data highlight advantages of using base editors as a mutagenesis tool and extend our current understanding of HBG gene regulation which may have future therapeutic relevance in SCD.

Strengths:

Genetic mapping of key regulatory elements within the HGB promoter region has been conducted using nuclease-based mutagenesis but is limited by the imprecise repair outcomes of NHEJ and by frequent deletion of the region intervening the highly homologous sequences of the duplicated HBG1 and HBG2 loci. The use of base editors is an elegant strategy to overcome these challenges.

Although nuclease-based indel induction can be readily used to disrupt regulatory elements, it is less useful for the introduction of novel transcription factor binding sites unless paired with inefficient homology directed repair approaches. The current study demonstrates a key advantage of base editing which is the ability to efficiently create transcription factor binding sites. This broadens the scope of functional alterations that can be introduced within gene regulatory elements.

In addition to demonstrating that base editors are a useful tool for regulatory element mapping, the current study sheds new light on the regulation of HBG expression by resolving five additional regulatory regions located within the HBG proximal promoter region that could have therapeutic relevance in SCD.

We thank the reviewer for the supportive words and the positive comments about our study.

Weaknesses:

Although the paper successfully identifies five novel regulatory regions within the HBG promoter that when modified increase HbF expression, none of these edits induce HbF as effectively as disruption of the previously identified BCL11A binding site. Thus, from a translational standpoint the results do not appear to be a significant advance. However, the substantial variation in editing at each site may be limiting the magnitude of HbF induction.

The reviewer brings up an excellent consideration. As the reviewer has rightly pointed out, one of the shortcomings in this study is to obtain equivalent editing efficiency in all the target regions and correlate the extent of editing with HbF levels. To overcome this problem, we have used ABE 8e (a variant with high processivity) to increase the editing efficiency of low editing gRNA with high HbF (Figure 3-Sup figure 4). Finally, we have evaluated a novel target site (gRNA 11) identified from this study with ABE 8e in healthy donor CD34+ HSPCs. As expected, we were able to obtain higher editing efficiency at the target site. Importantly, the corresponding increase in HbF levels was better than the current clinical trial target that disrupts the BCL11A binding site (Figure 4).

Variable editing across target sites has been described for both CBE and ABE, so it's not unexpected that it was observed in the current study. However, without data showing the transduction efficiency of the sgRNA vectors (by GFP flow) it is difficult to conclude that the variation is solely a function of the target or target sequence context. At a few of the sites with low genetic editing there is a significant increase in HbF expression, suggesting that if the editing at these sites were improved the functional induction of HbF could be substantial.

We appreciate the reviewer’s comment. We have now included the analysis of transduction efficiency for all the gRNAs as suggested by the reviewer. The data indicate that the variation in the HbF levels depends on the target site rather than the transduction efficiency (Figure 2 and Figure 3-Sup figure 1). Also, we have evaluated the low editing gRNA with high HbF using the ABE 8e variant, which significantly increased the editing efficiency and the HbF levels (Figure 3-Sup figure 4).

The use of lentivirus delivered gRNAs would appear to be amenable to pooled screening, but in the current study the screen was conducted in an arrayed fashion. The ability to conduct a pooled screen could substantially expand the utility of this approach and it would have been very interesting to see how the results from a pooled and arrayed screen compared to one another.

This is an interesting suggestion. Previous work published by Kim et al., (Genome Res. 2018 Jun; 28(6): 859-868) showed that arrayed CRISPR based screen can be more sensitive than a pooled screen. Their group has pointed out that subtle phenotypic effects may not be observable in a pooled screen when compared to an array screen, as the samples in arrayed screens exhibit clear phenotypic variation even with minor editing. Another study (Raphaella W L So et al., Mol Neurodegener. 2019 Nov 14;14(1):41.) has reported that cells in a pooled culture can negatively affect other cells particularly when they undergo inflammatory response or senescence after editing, this is not the case in arrayed screen as the individual targets are separated.

Lastly, while the functional readouts in the current study are compelling and largely correlate with the editing, confirmation in a second system would further inspire confidence in the results. In particular, members of this team and others have carried out editing in primary human CD34+ hematopoietic stem/progenitor cells (HSPC) followed by erythroid differentiation. Confirmation of the identified targets in primary cells with therapeutic relevance would inspire further confidence in the results.

We appreciate the reviewer’s comment. We have now performed base editing in CD34+ HSPCs and were able to achieve HbF levels at a therapeutically significant level (Figure 4).

– GFP flow data for the transduction efficiency of sgRNA vectors should be included. This is critical to confirm that the editing variability is due to target site and not transduction efficiency. It would also be helpful to confirm that none of the gRNA sequences contain premature termination sequences as well.

We agree entirely with the reviewer and have included the plot for the transduction efficiency of individual gRNAs with their respective editing efficiency at the target site (Figure 2c and d and Figure 3-Sup figure 1f). We also thank the reviewer for bringing the premature termination sequences to our attention. We have verified that there are no premature termination sequences in the gRNAs used in our study.

– The nature of the replicates are unclear. In figures 2 and 3 there are no error bars and in the figures with error bars it in not clear the definition of the biological replicates. Were these independent transductions of the cells, or repeat measures of the same transduced populations?

We apologize for the confusion. We performed the first round of screening for the 41 gRNAs in two different base editor expressing HUDEP-2 cell lines (ABE and CBE) without replicates (data in Figure 2c and d). After performing the screen, the top 8 gRNAs based on HBF expression were taken for further validation in triplicates with independent transduction of cells (Figure 3).

– In the functional validation experiments, were these independent transductions or use of the initial transduced populations? What were the sgRNA transduction rates in the populations used?

Thank you for this comment. The functional validation experiments were performed as three independent transductions of cells. The transduction efficiency of all the gRNAs were presented in Figure 3-Sup figure 1f.

– Several previous editing strategies have been used to increase HbF. It would be informative if prior editing strategies were included in this study for comparison.

This is an excellent suggestion. We have compared the editing of previously identified BCL11A binding site with the Cas9 and two different base editors (CBE and ABE) in parallel to evaluate the fetal globin expression in HUDEP-2 cells. Indeed, we observed the higher frequency of large deletions (encompassing HBG2 gene) with the lower level of G γ chain expression in Cas9 edited cells in comparison with ABE and CBE. These results further validate the conclusion that ABE and CBE are highly efficient in editing the highly homologous regions like γ globin promoter without causing any major deletions. We have added the new figure (Figure 1) and relevant descriptions based upon these results in the revised manuscript.

– Quantification of the EMSA in figure 6c should be included.

Thank you for this comment. As suggested by the reviewer, we have quantified the EMSA bands based on its intensity and included these data in Figure 5d and Figure 5-Sup figure 1b.

– Additional analysis to quantify and more clearly represent the correlation between genome editing and HBG (HbF) induction would be helpful for readers.

We completely agree with the reviewer. We have now represented the correlation between genome editing and HbF induction more clearly using principal component analysis (PCA) in 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. 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.

– Follow up validation of promising targets using transient transfection approaches would be informative as well.

We thank the reviewer for this comment. To this end, we have performed base editing of promising target regions in human CD34+ HSPCs by electroporation of ABE8e mRNA with gRNA-2 or gRNA-11 that targets the BCL11A binding motif at position −118 to −114 position and the putative KLF1 consensus motif at -123 and -124 position in the HBG1/HBG2 gene promoters respectively. We cultured the base- edited CD34+ HSPCs under erythroid differentiation conditions and determined the effect of base editing on HbF expression, erythroid differentiation, and enucleation. We have now included the comprehensive analysis of the base editing healthy donor CD34+ HSPCs by transient transfection in the revised manuscript (Figure 4).

– Despite low indels the frequency of deletion between HBG1 and 2 by PCR should be measured and the gel included next to a nuclease control.

Thank you for the thoughtful comment. As per the reviewer’s suggestion, we have performed the 4.9kb deletion analysis in the edited samples by using qRT PCR as previously reported by André Lieber’s group (Chang Li et al., Blood. 2018 Jun 28;131(26): 2915-2928.). We have validated the 4.9kb large deletion frequency (encompassing HBG2 gene) for ABE, CBE and Cas9 in HUDEP-2 cell lines targeting the BCL11A binding site at the highly homologous HBG promoter. The Cas9 edited cells resulted in higher frequency of 4.9 kb deletion as anticipated. Interestingly, we have observed the deletions in the base edited cells but significantly lower when compared to Cas9 (Figure 1c). To extend this finding, we measured the frequency of the larger deletion for the other potential target sites that resulted in lower level of HBG2 chain expression (Figure 3-Sup figure 3e and k) and also in base edited CD34+ HSPCs cells (Figure 4c).

– ABE gRNA 03 gave low genetic editing rates but apparently higher HbF induction. it would be very interesting to see how this target behaved if targeted with recently published hyperactive (ABE8e etc) variants. If high editing were achieved it would seem this could be an attractive target for therapy.

Thank you for this comment. We have now evaluated the gRNA-3 which resulted in higher induction of HbF with low editing efficiency using the hyperactive variant ABE8e and subsequently analyzed for the further possible increase in HbF expression by increasing the editing efficiency in HUDEP-2 cells. The ABE8e variant has greatly increased the base editing efficiency at the target site and resulted in higher levels of HbF induction with reduced frequency of the larger deletion in comparison with ABE7.10 (Figure 3-Sup figure 4d). This result suggests that adenine base editing of the HBG1 and HBG2 promoters to create the -175 T>C change with ABE 8e is a potential strategy for the therapeutic induction of fetal globin level and treatment for sickle disease and β thalassemia.

– Additional analysis to quantify and more clearly represent the correlation between genome editing and HBG (HbF) induction would be helpful for readers.

– For promising targets, confirmation in primary CD34+ HSPC would increase the impact of the current study.

We thank the reviewer for the great suggestion. As detailed above in Response 1.7, we analyzed the therapeutic potential of novel targets (gRNA-11) identified from this study on induction of γ globin expression by base editing of CD34+ HSPCs from a healthy donor (Figure 4). Base editing of the -123 region of HBG promoter resulted in a therapeutic level of induction of HbF in erythroblasts derived from human CD34+ HSPCs, without having any detrimental effects on erythroid differentiation and enucleation. Notably, the level of HbF induction was more pronounced with the installation of novel -123 cluster HPFH like mutations than the creation of -115 cluster HPFH mutation which disrupts the BCL11A binding site. The results indicate that base editing at the -123 region of HBG promoter may offer a new therapeutic approach for the treatment of β-hemoglobinopathies.

– The observation of editing outside the spacer window described as novel in line 188 has been previously observed: Webber et al., Nature Comm (https://www.nature.com/articles/s41467-019-13007-6), Arbab et al., Cell (https://www.sciencedirect.com/science/article/abs/pii/S0092867420306322). These studies should be cited and text changed to describe these results as confirmatory.

We apologize for the mistake. We have now added the relevant citations and rephrased the sentence in the Results section.

– Line 221 describing reduction of double positive cells in CBE treated cells. Since results are not significant this statement should be removed.

We agree entirely with the reviewer and have removed the statement in the revised manuscript.

Reviewer #2:

In this study, the Authors use base editors to generate mutations in the promoters of the genes encoding the γ-globin chains of the fetal hemoglobin in an adult erythroid cell line. Several of these mutations are associated with increased γ-globin gene expression, paving the way for the development of therapeutic strategies for β-hemoglobinopathy patients who have been shown to benefit from persistent expression fetal hemoglobin in adulthood. The major strengths of this work are: (i) the discovery of novel mutations associated with elevated feta hemoglobin levels; (ii) the impact of these discovery in the field of gene therapy for β-hemoglobinopathies.

We thank the reviewers for highlighting the importance of our study, as we also believe that it will advance the field of gene therapy for β-hemoglobinopathies.

The weakness of this study is the technical quality that can be improved to support the Authors' conclusions.

We appreciate the reviewer’s constructive comment. As per the reviewer’s suggestion, we have worked on the technical quality of our study and manuscript to further improve and support our conclusions.

Remarks to the Authors:

– While many findings of this study are interesting, in the Results section it is not clear which are the specific questions. The results are a list of data that are partially interpreted only in the Discussion. I would suggest the Authors to better explain their findings in the Results section (e.g., comparison of different strategies disrupting repressor binding sites or recruiting transcriptional activators).

We thank the reviewer for the great suggestion. We agree entirely with the reviewer and acknowledge that in the first version of the manuscript we did not adequately interpret the results. Prompted by the comment of the reviewers we have reworked the current version of the manuscript in the Results section to include a detailed explanation of the findings and interpreted the results more clearly as suggested.

– It would be interesting to study if any of the mutations that do not increase HbF, down-regulate γ-globin expression (in cells that do express HbF) to identify binding sites for activators.

It is a very interesting perspective that the reviewer has pointed out. To examine this observation, we have selected the gRNAs that have higher editing efficiency at the target site with the decreased expression of γ-globin from our preliminary screen. These gRNAs were screened in K562 cell line (which has higher basal level of HbF) with the base editors to identify the potential activator binding sites in the HBG promoter. The percentage of HbF positive cells remains unaltered after editing suggesting that the targeted regions did not have binding sites for transcriptional activators. This new data is now discussed in revised version the manuscript (Figure 2-Sup figure 3).

– The Authors should better characterize the cell lines expressing base editors. If I understood correctly, these cells are constitutively expressing the base editors (and the gRNAs). Some base editors are known to induce off-targets at DNA and RNA levels. This possibility should be investigated (at least the RNA off-targets) to verify that the major players in HbF regulation are properly expressed by performing a side-by-side comparison of cells expressing the base editors and untransduced cells. In addition, if base editors and gRNA are constitutively expressed, base editing efficiency can change overtime and should be evaluated in the same cells in which the Authors measure HbF expression.

We thank the reviewer for the excellent suggestion. We used the constitutively expressing base editor along with the gRNA for our experiments. We have now conducted the transcriptome profiling of base editor stable cell line (both ABE7.10 and BE3) and compared with the wild type HUDEP-2 cells. The data presented in the new figure (Figure 1-Sup figure 1 (b)) shows that the gene expression profiles are not altered and exhibit a significant correlation with the wild type HUDEP-2 cells. We have also characterized the base editor stable cell lines for the editing efficiency at different time points (in days) and its effect on HbF expression using the known gRNA-2 (targeting BCL11A binding site). The editing efficiency and HbF levels in both ABE and CBE increases over time with no discernible effect on erythroid differentiation (Figure 1-Sup figure 1 (c-h)). As the reviewers have suggested, we have also investigated the possible DNA and RNA off target effects and analyzed the expression of major molecular targets that are involved in HbF regulation in the base edited samples. Transcriptome wide RNA sequencing on ABE and CBE stables with gRNAs 2 or 11 confirms that the distribution frequency of A-to- I (in ABE) or C-to-U (in CBE) conversion across the base edited samples were very similar to that of the parental stable cell line (Figure 6b-e). The Cas-dependent DNA off target were also validated, we were not able to observe any significant undesired off target effects despite higher on target editing efficiency (Figure 6a). We performed the differential expression analysis for the 34 selected genes that are involved in globin regulation and observed no significant difference in base edited cells compared to the control (Figure 6-Sup figure 1).

– Evaluation of HbF+ cells (by flow cytometry) and globin mRNAs (by qRT-PCR) should be performed in differentiated cells, as Hb expression changes upon differentiation.

Thank you for this comment. We agree with the reviewer that HbF expression will change upon erythroid differentiation. As the reviewer suggested, we have determined the level of globin mRNA by qRT-PCR and HbF positive cells by flow cytometry for the top 8 targets in the ABE and CBE edited cells after erythroid differentiation (Figure 3c and d, Figure 3-Sup figure 3b and h ). The number of HbF positive cells in differentiated erythroid cells was slightly higher than that of the undifferentiated cells. The relative expression of γ globin in the erythroid differentiated samples followed a similar trend to that of the undifferentiated sample.

– The Authors should specify if control cells express the base editors and a control gRNA.

Thank you for this comment. The control cells used in our study constitutively express the base editors and the control gRNA (with scrambled sequence) in HUDEP2 cells. In case of CD34+ HSPCs, we used the gRNA targeting the AAVS1 site (a safe harbour locus) along with base editor mRNA as a control. We have added the relevant information in the revised manuscript as suggested by the reviewer.

– The first screening of gRNAs is performed without replicates (Figure 2 and 3 and related supplementary figures). Furthermore, many of the gRNAs produce very few editing events, thus it is not possible rule out if the target regions are important for γ-globin expression. Therefore, I would exclude from the analysis the gRNAs associated with a low genome editing as these results are confounding and do not give insights in the regulation of γ-globin expression. Maybe Figure 2 and 3 (and related supplementary figures) could be kept as supplementary data.

We thank the reviewer for the suggestion. We agree entirely with the reviewer that the target region of many of the low editing gRNAs might have a possible role in γ globin regulation. Therefore, we excluded the gRNAs-37,38,7,18,19 and 29 from ABE and gRNAs-1,35,37,6,7,13 and 19 from CBE from the analysis because of the lower editing efficiency (<10%) at the target site. We have moved the figure 2 to the supplementary figures (Figure 2-Sup figure 1a and c) as suggested by the reviewer. Figure 3 was kept in main figure (as Figure 2) as it is to represent the level of HbF induction with the total editing efficiency for all the gRNAs from the primary screening.

– Line 178: the -117 mutation (occurring upstream of the gRNA target site) should be reported in Figure 4.

We appreciate the reviewer’s suggestion on including the base conversion events that occurred upstream of the protospacer target site. We have now represented all these mutations in the new figure (Figure 3-Sup figure1b) as suggested.

– Interestingly, the Authors observed that the -123/-124 mutations create a binding site for KLF1. However, most of the editing events generate only one of the 2 mutations (figure 4 suppl 2), would individual mutations be sufficient to generate a KLF1 binding site? Can this be tested by performing an EMSA assay with oligos harboring individual mutations? If individual mutations are not generating a KLF1 binding site, would the observed HbF reactivation be justified by the recruitment of KLF1 only to a minor fraction of the alleles harboring the concomitant editing of the 2 nucleotides?

Finally, can the Authors perform ChIP experiments to demonstrate the increase KLF1 recruitment upon editing of the -123/-124 region?

We thank the reviewer for the excellent suggestion. We have performed a new EMSA to determine the effect on KLF1 binding to a probe containing the individual and combination of -123/ -124 mutations along with the wild type probe. The results indicate that the combination of -123 T>C and -124 T>C mutation (but not with the -123 T>C or -124 T>C individual mutation) is required for the KLF1 binding to the HBG promoter in vitro (Figure 5c-d, Figure 5-Sup figure 1a and b). To substantiate this finding, we improved the editing efficiency at the target site with the hyperactive variant of ABE (ABE 8e) and gRNA-11 which results in the higher proportion of -123 T>C and -124 T>C combination mutations with substantial increase in HbF expression in the CD34+ HSPCs (>90% base substitution at 123 T>C and -124 T>C position with >90% HbF+ cells) (Figure 4b and e). The ChIP results were not conclusive. They did not show a significant enrichment of KLF-1 at the 123 T>C and -124 T>C mutated region of HBG promoter when compared to an arbitrarily chosen negative control (VEGFA) but did exhibit slightly better enrichment compared to the WT clones (Figure 5-Sup figure 1c). It should be noted that as seen in the EMSA the KLF1 binding is at best weak and may be below the level of detection by ChIP in these experiments. Future investigations would be required to confirm that KLF1 binding to this site is the main in vivo mechanism of -123 T>C and -124 T>C HPFH driven up-regulation of γ globin. We have worded our manuscript carefully to make this point clear.

– Lines 311-314: "CBE mediated base conversion (C to T) at positions – 114 and -115 resulted in significantly greater induction of HbF than the multiple A to G nucleotide substitutions at -110, -112, -113, -116 positions made by ABE." I believe that this or similar statements should be supported by the analysis of clonal populations harboring the same number of edited promoters.

We respectfully suggest that the effect of individual mutations at the -115 clusters of HBG promoter on γ globin regulation are extensively characterized by previous publications (Martyn et al., Nature genetics 2018., Liu N et al., Cell 2018). Further, we believe that the change in HbF induction by ABE and CBE are mainly due to the variation in the binding affinity of BCL11A towards the mutated core TGACCA motif. The previous publication by Yang et al., (Cell Research (2019) 29:960– 963) proves that the nucleotides at -114 and -115 positions are very important for BCL11A binding when compared to other nucleotides at the -115 region of HBG promoter, which is highly consistent with our observations of CBE mediated base conversion at – 114 and -115 position.

– Figure 3-Suppl Figure 1: it would be helpful to plot HbF expression of the negative controls.

Thank you for this comment. We have included the HbF expression of the negative control and presented in the new updated Figure 2 c and d (previously Figure 3-Sup figure 1) of the revised version of manuscript as suggested by the reviewer.

– SEM is missing in Figure 4-suppl Figure 1, panels A and C.

Thank you for this comment. We have included the SEM for the updated new Figure 3a and b (previously Figure 4-Sup figure 1) as suggest by the reviewers.

– Figure 5-Suppl Figure 1, panel d: why α and β chains are reduced in samples gRNA02 and gRNA 42?

This is a great question. We believe based on the previous report that the higher induction of γ globin chain is shown to downregulate β globin chain expression thereby maintaining the α to β-like globin chain ratio (Huang P et al., Genes Dev. 2017). They have indicated that in fetal stages/HPFH condition, the LCR directly interacts with γ globin promoter for its regulation. Therefore, the LCR will not be able to access β globin promoter which results in reduced expression of β globin chain. In case of α globin, we are not entirely sure about the mechanism, but previous reports have shown that induction of HbF using inducers have down regulated α globin chain expression (Khamphikham P et al., Br J Haematol 2020).

– Figure 5-Suppl Figure 1: Can the Authors comment on the imbalanced Agamma/Ggamma ratio in sample gRNA 42? Could it be due to the potential deletion of the HBG2 gene? Besides Indels, did the Authors measure the frequency of the 4.9kb deletions in base-edited samples?

We thank the reviewer for bringing up an excellent point. We are not entirely sure about the reason for variation in A γ and G γ expression in gRNA 42. As reviewers suggested, we have now performed the qRT-PCR analysis on CBE stables cells transduced with gRNA-42 to ensure that the difference in γ chain expression is due to 4.9kb large deletion. We did not observe the large deletion in gRNA 42 edited cells (Figure 3-Sup figure 3k). Thus, the decrease in G γ chain expression is independent of the large deletion and might be due to the biased expression of the γ globin.

– It would be interesting to perform experiments in primary cells to validate these interesting findings.

We thank the reviewers for the great suggestion. As detailed above in Response 1.7, we performed the base editing to introduce novel -123 cluster mutations at the HBG promoter in heathy donor CD34+HSPCs. Targeted adenosine base editing at the -123 region of the HBG promoter induced robust γ globin expression (than the base editing at the BCL11 binding site) in CD34+ HSPC-derived human erythroblasts, without having any detrimental effect on erythroid differentiation and maturation (Figure 4).

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

• 3,453

  Page views

• 470

  Downloads

• 13

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.