Abstract
Genome-wide association studies (GWAS) have identified thousands of variants associated with human diseases and traits. However, the majority of GWAS-implicated variants are in non-coding regions of the genome and require in depth follow-up to identify target genes and decipher biological mechanisms. Here, rather than focusing on causal variants, we have undertaken a pooled loss-of-function screen in primary hematopoietic cells to interrogate 389 candidate genes contained in 75 loci associated with red blood cell traits. Using this approach, we identify 77 genes at 38 GWAS loci, with most loci harboring 1-2 candidate genes. Importantly, the hit set was strongly enriched for genes validated through orthogonal genetic approaches. Genes identified by this approach are enriched in specific and relevant biological pathways, allowing regulators of human erythropoiesis and modifiers of blood diseases to be defined. More generally, this functional screen provides a paradigm for gene-centric follow up of GWAS for a variety of human diseases and traits.
Introduction
As genotyping technologies and accompanying analytical capabilities have continued to improve, genome-wide association studies (GWAS) have identified tens of thousands of variants associated with numerous human diseases and traits. Despite these advances, our ability to discern the underlying biological mechanisms for the vast majority of such robust associations has remained limited, with a few exceptions (Claussnitzer et al., 2015; Gupta et al., 2017; Mohanan et al., 2018; Musunuru et al., 2010; Sankaran et al., 2008; Smemo et al., 2014). In general, published successes have required in-depth mechanistic studies of individual loci and implicated genes to decipher biological mechanisms.
Recent innovations in functional and computational genomics have advanced the field and enabled more rapid and higher-throughput identification of putative causal variants. Approaches that have shown the most success include the use of massively parallel reporter assays to examine allelic variation (Tewhey et al., 2016; Ulirsch et al., 2016; Vockley et al., 2015) and perturbation approaches for dissecting the necessity of regulatory elements (Fulco et al., 2016; Simeonov et al., 2017). In addition, genetic fine mapping approaches have improved our ability to identify putative causal variants among larger sets of variants in linkage disequilibrium (Guo et al., 2016; Huang et al., 2017; Lareau et al., 2018). However, even when putative causal variants are identified at a disease or trait-associated locus, they most often localize to non-coding regions of the genome, making it difficult to connect variants to genes that mediate the observed effects in a scalable manner (Claussnitzer et al., 2015; Gupta et al., 2017; Smemo et al., 2014).
In the context of hematopoiesis, GWAS studies have identified thousands of variants associated with various blood cell traits, including hundreds associated with red blood cell traits alone (Astle et al., 2016; van der Harst et al., 2012). Thorough follow-up efforts at individual loci have identified important regulators of hematopoiesis, such as the key regulator of fetal hemoglobin expression, BCL11A (Basak et al., 2015; Liu et al., 2018; Sankaran et al., 2008). However, as in other tissues, the low-throughput with which associated genetic variants can be connected to target genes underlying phenotypes continues to pose a problem for gaining biological insights and clinical actionability in complex traits and diseases.
To accelerate the rate at which genetic variants can be connected to target genes, high-throughput loss-of-function screens involving putative causal genes underlying the genetic associations can be undertaken. This approach is complementary to conventional variant-focused methods and overcomes bottlenecks that can arise during downstream target gene identification. As a proof-of-principle, we connected variants associated with RBC traits to genes regulating erythropoiesis by directly perturbing all candidate genes in primary human hematopoietic stem and progenitor cells (HSPCs) undergoing synchronous differentiation into the erythroid lineage. We demonstrate unique opportunities to rapidly implicate likely causal genes and identify networks of biological actors underlying trait-associated variation. We additionally illustrate the value of such screens to uncover previously unappreciated biological regulators of human hematopoiesis that may serve as key disease modifiers.
Results
Design and Execution of an shRNA Screen Using Blood Cell Trait GWAS Hits to Identify Genetic Actors in Erythropoiesis
We applied a gene-centric loss-of-function screening approach to GWAS of RBC traits. We focused on 75 loci associated with RBC traits that were identified by a GWAS performed in up to 135,000 individuals (van der Harst et al., 2012) spanning 6 RBC traits (Figure S1A). Importantly, these 75 loci have been robustly replicated and show large effect sizes in more recently reported association studies performed on larger cohorts and thus represent ideal targets for perturbation studies (Astle et al., 2016; Lareau et al., 2018). We endeavored to select all genes that could potentially underlie these 75 GWAS signals. To do this, each of the 75 sentinel SNPs was first expanded to a linkage disequilibrium (LD) block including all SNPs in high LD (r2 > 0.8, Figure 1A, Figure S1B), then further to the nearest genomic recombination hotspot. Based upon insights from previous expression quantitative trait locus (eQTL) studies (Montgomery and Dermitzakis, 2011; Rossin et al., 2011; Veyrieras et al., 2008), each gene annotated in the genome was expanded to include a wingspan encompassing 110 kb upstream and 40 kb downstream of the transcriptional start and end sites, respectively, to also capture potential functional regulatory elements. This resulted in selection of 389 genes overlapping or in the vicinity of the LD blocks to be tested in the pooled loss-of-function screen. These were distributed at a median of 4 genes per loci (Figure S1C).
Since the majority of common genetic variation underlying RBC traits appears to act in a cell-intrinsic manner within the erythroid lineage, we decided to perturb the candidate genes during the process of human erythropoiesis (Giani et al., 2016; Sankaran et al., 2012; Sankaran et al., 2008; Ulirsch et al., 2016). We chose a pooled short hairpin RNA (shRNA) based loss-of-function approach in primary hematopoietic cells to leverage a number of distinct strengths. First, we have had prior success validating individual genes underlying RBC traits using shRNA-based approaches in primary CD34+ HSPC-derived erythroid cells. Second, shRNA libraries can be much more efficiently packaged into lentiviruses and delivered to primary CD34+ cells compared to alternative CRISPR/Cas9-based guide RNA libraries (Ting et al., 2018). Third, it avoids potential complications like non-uniform loss-of-function or gain-of-function outcomes produced by CRISPR/Cas9 based approaches due to unpredictable DNA repair processes (Mandegar et al., 2016). Furthermore, shRNAs can act rapidly to achieve gene knockdown and thereby avoid compensatory effects that can occur when complete CRISPR knockout is achieved (Rossi et al., 2015), better recapitulating the subtle changes in gene expression that are characteristic of common genetic variation.
Mobilized peripheral blood-derived primary human CD34+ HSPCs from 3 independent healthy donors were infected with a lentiviral-based pooled shRNA library consisting of 2803 hairpins targeting the 389 GWAS-nominated genes, along with 30 control genes (Moffat et al., 2006). Each gene was targeted with 5-7 distinct shRNAs (Figure S1D). The set of control shRNAs encompassed essential control genes, negative controls (e.g. luciferase and other non-expressed genes), and a well-defined set of genes important for erythropoiesis (erythroid controls) (Figure 1B, Table S2). Using lentiviral libraries with defined titers, we achieved an infectivity of 35-50%, which is optimal for obtaining either zero or one stably integrated shRNA per cell, while minimizing the possibility of infection of a single cell by multiple viruses. To achieve sufficient library representation, we infected at least 1000 CD34+ HSPCs per hairpin (7~11 * 106 cells per experiment). The infected HSPCs were cultured using a three-phase erythroid differentiation method (Giani et al., 2016; Hu et al., 2013) that results in synchronous differentiation and maturation of erythroid progenitors into RBCs. We hypothesized that hairpins targeting potential regulators of erythropoiesis would be depleted or enriched during the three-phase erythroid culture, similar to our prior experience in analyzing specific GWAS-nominated genes (Giani et al., 2016; Sankaran et al., 2012; Ulirsch et al., 2016). To assay these hairpins, we isolated and deep-sequenced genomic DNA from the pool of infected cells at 6 different culture time points that represent distinct stages of erythropoiesis to most broadly assess putative causal genes that may act across the span of differentiation (Figure 1C, S1E).
Summary Characterization of shRNA Screen Outcomes
For the vast majority of the ~3000 hairpins included in the library, infection was efficient and consistent. Greater than 95% of hairpins were represented at levels of at least 5 log2 counts per million (CPM) at day 4, two days post-infection (Figure 2A). Across the two-week time course, a diversity of effects - in terms of both increased and decreased hairpin abundance - were observed. While many hairpins were selected against during the course of erythroid differentiation, as reflected in decreases of those hairpin abundances over time, there were also a number of hairpins that increased in the culture over the time course (Figure 2B, S2A).
The tested set of hairpins targeting genes nominated by the 75 loci showed a variety of activities, forming a broad distribution spanning both decreases and increases in abundance at different time points (Figure 2C). The various controls included in the library behaved as expected. Hairpins targeting genes with known significance to erythropoiesis, such as GATA1 and RPS19 (Khajuria et al., 2018; Ludwig et al., 2014), showed markedly decreased abundance across the time course. Likewise, hairpins targeting a set of broadly essential genes (Table S2) were strongly depleted by day 16 when compared to negative control hairpins targeting non-human genes, which showed little if any change (Figure 2C-E). These trends were recapitulated with strong correlation in each of the three donor CD34+ cell backgrounds (Figure S2B).
Statistical Modeling of Gene Effects and Accounting for Confounders in the shRNA Screen
The resulting longitudinal observations of hairpin abundance at each time point were used to model the importance of each targeted gene during the process of erythropoiesis. A linear mixed model was implemented to account for the longitudinal nature of the time course data (Li et al., 2015) and to handle the confounding off-target and efficiency effects inherent to the shRNA modality (Riba et al., 2017; Tsherniak et al., 2017). Since we wanted our model to be able to detect significant changes in hairpin abundance at any time point throughout the differentiation process, we converted the absolute hairpin abundances at each of the six time points to a log2 fold change relative to the initial hairpin abundances at the start of the differentiation. Using this metric as our response variable, we specified a fixed effect for each gene to capture the contribution that suppressing it with shRNAs would have on the respective abundances for each of the resulting five time intervals. Given the potential variability that could emerge by using shRNAs, we fit a random effect for each hairpin to minimize the chance of conflating inefficiency or off-target effects with the specific on-target gene effect.
After fitting this model to the data, we selected our hit set using a two-threshold approach in which both the magnitude and statistical confidence of the estimated gene effect size were considered. Specifically, genes were called as hits if they had a fitted slope > 0.1 log2 fold change per day within the interval while simultaneously possessing a Wald chi-square FDR-adjusted q value < 0.1. This combined approach allowed us to avoid focusing on genes with large, but highly variable or conflicted effects, as well as genes with highly confident but miniscule effects. In total, this approach identified 77 genes at 38 of the 75 targeted loci which, when suppressed, had a significant effect on the slope of shRNA-encoding DNA abundance at any point during the time course. A majority of these hit loci (27 loci) had 1-2 gene targets prioritized (Figure 3A, S3A). These candidate genes were found to be distributed across all 6 of the originally annotated RBC GWAS traits (Figure S3B).
To evaluate the validity of this hit set, we began by assaying for enrichment of erythroid essentiality, as recently quantified for each gene in the K562 erythroid cell line (Wang et al., 2015). A permutation comparing the sum of K562 essentiality scores for the hit genes with those of randomly drawn, identically-sized gene sets from the library of targeted genes revealed that the hit set was indeed enriched with p = 0.0269 (Figure 3B). Likewise, when compared to permuted sets of 77 genes randomly chosen from the genome (Figure S3C), there was even stronger enrichment for erythroid essentiality with p = 0.00021, consistent with the idea that genes in the library likely have stronger essentiality due to their genomic proximity to the GWAS hits. We further explored whether the enrichment could be due to an intrinsic bias inherent to GWAS screening itself by permuting sets of genes from libraries nominated by SNPs associated with low-density lipoprotein levels, high-density lipoprotein levels, and triglyceride levels, finding the hit set to be significantly enriched in all comparisons (Willer et al., 2013) (Figure S3D-F).
We further validated the ability of this approach to discover genetically relevant hits by performing a permutation analysis based upon five “gold standard” genes in the library, which possess known genetic underpinnings via identified causal variants: CCND3 (Lareau et al., 2018; Sankaran et al., 2012), SH2B3 (Giani et al., 2016), MYB (Galarneau et al., 2010; Sankaran et al., 2013; Sankaran et al., 2011), KIT (Jing et al., 2008; Lareau et al., 2018), and RBM38 (Ulirsch et al., 2016). Calculating the rank sums of hairpins ordered by our model’s computed FDR scores for 1,000,000 random combinations of five genes from the library yielded a distribution over which enrichment for the five gold standards was seen with p=0.0249 (Figure 3C). While the vast majority of putative causal variants at the RBC trait-associated loci are in non-coding regions, which can be challenging to use to identify a specific target gene, a subset are in coding regions and thereby nominate a specific gene. As a result, we assayed for the presence of coding variants fine-mapped to the interrogated loci from a recent large GWAS that demonstrated a minimum posterior probability of association of 0.1 among the gene hits and compared this with the overall set of genes interrogated in our library (Lareau et al., 2018). Among the 389 GWAS-nominated genes in our library, 20 (~5%) were found to contain at least one coding variant from this list. Of these, there was a significant enrichment observed among the hits (~9%, p=0.03907 as determined by permutation analysis; Figure 3D).
Having established genetic confidence in our hit set, we next investigated whether the selected genes satisfied enrichment criterion within the erythroid branch of hematopoiesis. RNA expression values for each of the 77 hit genes were examined in datasets spanning human hematopoiesis (Corces et al., 2016), as well as adult and fetal erythropoiesis (Yan et al., 2018) (Figure 3E,F; Figure S3G). In the more holistic hematopoiesis dataset, common myeloid progenitors (CMPs) and megakaryocyte-erythroid progenitors (MEPs) were significantly enriched for hit genes (p < 0.01). These progenitor populations are known to contain the progenitors that give rise to erythroid cells. Within a more detailed and separate analysis of human adult erythropoiesis, proerythroblast, early basophilic, and late basophilic erythroblast stages were particularly enriched (p < 0.001). The stage at which given genes are implicated to play a role in erythropoiesis from the literature likewise often corresponded with the largest magnitude fold changes across the longitudinal time course measurements, as was the case for earlier genes like RPL7A, RPL23A, RPS19, and KIT (Gazda et al., 2012; Jing et al., 2008; Moniz et al., 2012) as well as late genes like SLC4A1 and ANK1 (Bennett and Stenbuck, 1979; Peters et al., 1996). Taken together, these results show that this functional gene-centric screen allows for the identification of putative causal genes underlying RBC-trait GWAS hits, which demonstrate clear enrichment in independent genetic and cell biological datasets. We are therefore able to validate the utility of such an approach to identify biologically-relevant genes underlying human genetic variation and holistically identify potential stages by which such target genes may act to impact the process of hematopoiesis.
Analysis of Interactions Among Members of the Hit Set Identifies Signaling, Structural, and Translation-Related Subnetworks Important to Erythropoiesis
By screening all loci and genes at once, our approach afforded us the immediate value of examining mechanisms underlying the associations in a holistic fashion, unearthing both familiar and more novel core gene network cassettes that play a role in erythropoiesis (Boyle et al., 2017). Using STRING interaction network analyses (version 10.5) (Szklarczyk et al., 2017), we could identify connectivity between the underlying nodes that highlighted a number of interacting biological processes of both known and previously unappreciated importance to erythropoiesis (Figure 4). We observed a number of molecules that play roles in cell signaling or transcriptional regulation. MYB is a master regulator transcription factor that has been implicated in playing a role in fetal hemoglobin regulation and in erythropoiesis more generally (Mucenski et al., 1991; Wang et al., 2018). The MYB locus has been associated with numerous red blood cell traits (including mean corpuscular volume, mean corpuscular hemoglobin concentration, and RBC count) (Sankaran et al., 2013; van der Harst et al., 2012). ETO2 (CBFA2T3) is a part of the erythroid transcription factor complex containing TAL1 and is required for expansion of erythroid progenitors (Goardon et al., 2006). Both stem cell factor receptor KIT and erythropoietin receptor (EPOR) mediated signaling are essential for erythropoiesis. Our screen identified KIT as one of the factors underlying common genetic variation. CCND3 fills a critical role in regulating the number of cell divisions during terminal erythropoiesis and has been validated as a causal gene associated with variation in RBC counts and size (Lareau et al., 2018; Sankaran et al., 2012).
Interacting networks of hits also emerged in other aspects of red blood cell differentiation and function. One of these centered around membrane and structural cytoskeletal proteins. Our method recovered characteristic RBC genes like solute carrier family 4 member 1 (SLC4A1), also known as band 3, (Peters et al., 1996), which serves as a key component of the RBC membrane skeleton. Likewise, it recovered a direct interacting partner for SLC4A1, ankyrin 1 (ANK1), which anchors the cytoskeleton and cell membrane (Bennett and Stenbuck, 1979), as well as N-ethylmaleimide Sensitive Factor, vesicle fusing ATPase (NSF), which facilitates membrane vesicle trafficking within the cell (Glick and Rothman, 1987).
Within the realm of mRNA translation, a number of genes emerged as hits that specifically highlight the role of the ribosome. This is interesting in light of recent work that has begun to illuminate erythroid-specific effects of ribosomal perturbations (Khajuria et al., 2018; Ludwig et al., 2014), although a connection between translation and common genetic variation affecting RBC traits has not been previously appreciated. Both RPL7A and RPL19, for instance, have been implicated by mutations observed in studies of Diamond-Blackfan anemia (Gazda et al., 2012; Moniz et al., 2012). The common genetic variation affecting these ribosomal protein genes might contribute to the incomplete penetrance and variable expressivity of anemia seen in Diamond-Blackfan anemia patients (Ulirsch et al., 2018). Similar effects have been reported in neurodevelopmental disorders, where common genetic variants may influence phenotypic outcomes in patients (Niemi et al., 2018). Non-ribosomal hits in the mRNA metabolism space were also found with both previously established and unknown ties to erythroid-specific phenotypes. Exosome component 9 (EXOSC9), for instance, has been demonstrated previously to act as part of the exosome complex as a specific gatekeeper of terminal erythroid maturation (McIver et al., 2014). Other unappreciated components, including the tRNA methyltransferase TRMT61A, also were highlighted through this analysis.
Transferrin receptor 2 is a Negative Regulator of Human Erythropoiesis
We selected several candidate genes identified by our screen for further validation, given their previously unappreciated roles in human hematopoiesis/erythropoiesis. The first, transferrin receptor 2 (TFR2), encodes a protein canonically involved in iron homeostasis that has recently been shown to also regulate EPO receptor signaling (Forejtnikova et al., 2010; Nai et al., 2015). Although TFR2 has been studied in the context of murine erythropoiesis, its role in human erythropoiesis has not been assessed. To validate TFR2 as a regulator of human erythropoiesis, we performed individual knockdown experiments using lentiviral shRNAs in primary human CD34+ HSPCs undergoing erythroid differentiation. Significant knockdown of TFR2 was observed at both the mRNA (Figure 5A) and protein levels (Figure 5B) using two independent shRNAs from among the six targeting TFR2 in the screen. Though two of the six were outliers, the two chosen here for follow-up were part of the consensus group of four showing similar effects. Downregulation of TFR2 increased erythroid differentiation as observed by increased expression of erythroid specific cell surface markers CD235a and CD71 at day 9 (shLUC ~22%; TFR2 sh1 ~42%; TFR2 sh2 ~40%) and day 12 of culture (shLUC ~60%; TF2 sh1 ~80%; TFR2 sh2 ~80%) (Figure 5C, E & S5A). Downregulation of TFR2 also improved the later stages of erythroid differentiation/maturation, as observed by an increased rate of enucleation and through assessment of cell morphology (Figure 5D & S5B). Previous studies have reported the isolation of TFR2 as a component of the erythropoietin (EPO) receptor complex (Forejtnikova et al., 2010). To test if downregulation of TFR2 can result in increased EPO signaling, we measured EPO-dependent STAT5 phosphorylation after TFR2 knockdown in UT7/EPO cells (Figure S5C). TFR2 downregulation resulted in significantly higher pSTAT5 phosphorylation in comparison to the control with EPO stimulation from 0.02 U/mL to 200 U/mL (Figure 5F). In addition, the maximal pSTAT5 response could be achieved within a shorter period of EPO stimulation upon TFR2 downregulation (Figure S5D). Given our findings that TFR2 is a negative regulator of EPO signaling, it may be an ideal therapeutic target for conditions characterized by ineffective erythropoiesis like β-thalassemia (Rund and Rachmilewitz, 2005). A recent study has supported this hypothesis, showing that Tfr2 downregulation is beneficial in a mouse model of β-thalassemia (Artuso et al., 2018).
SF3A2 is a Key Regulator of Human Erythropoiesis and is a Disease Modifier in a Murine Model of Myelodysplastic Syndrome
Extensive mRNA splicing occurs during the terminal stages of erythropoiesis (Pimentel et al., 2016). However, key regulators of this process remain largely undefined. Our study uncovered splicing factor 3A subunit 2 (SF3A2) in the subnetwork of erythropoiesis signaling and transcription hits (Figure 4). SF3A2 specifically was associated with maximal hairpin drop out at day 12 (FDR = 0.005) – a later time point in erythropoiesis. SF3A2 is a component of the U2SNRP complex whose binding to the branch point is critical for proper mRNA splicing (Gozani et al., 1996; Gozani et al., 1998). Knockdown of SF3A2 in primary human CD34+ HSPCs results in decreased cell numbers during erythroid differentiation starting from day 7 (Figure 6A-C). To measure early effects of SF3A2 and to exclude potential toxicity of puromycin selection, we replaced the puromycin resistance gene with a GFP encoding cDNA in the lentiviral shRNA constructs. We achieved similar infection (30~40% on day 6) at the early time points between controls (shLuc) and shRNAs targeting SF3A2 (Figure S6A). During erythroid differentiation, we observed a reduction in GFP-expressing cells comparable to the decreased cell numbers seen with the puromycin resistant constructs (Figure S6A). Decreased cell numbers were associated with decreased erythroid differentiation as measured by erythroid surface markers CD71 and CD235a (Figure 6D). We also observed an increase in non-erythroid lineages based on surface marker expression of CD11b (myeloid) and CD41a (megakaryocyte) (Figure S6B).
To identify the molecular mechanisms underlying the reduced differentiation of erythroid cells we sorted stage-matched CD71+/CD235+ cells and performed RNA-Seq analysis. We also ran this analysis in parallel for data from hematopoietic progenitors from patients with myelodysplastic syndrome (MDS), a disorder well-known for significant impairment in terminal erythropoiesis, either with or without somatic mutations in the related splicing factor SF3B1 (Obeng et al., 2016). Cells treated with shRNA to suppress SF3A2 were found to differentially express 6061 genes with an adjusted p value < 0.05 as compared to the shLuc control, whereas only 807 genes were differentially expressed given the same threshold cutoff in the MDS patients with an SF3B1 mutations compared to those without (Figure 6E). Genes from both the SF3A2 differentially expressed set and the SF3B1 differentially expressed set were significantly enriched for structural constituents of the ribosome (p < 3.2 × 10−44 and p < 7.5 × 10−24, respectively) among other cellular components and functions (Tables S6-S9). Examining differential splicing in the set of genes not differentially expressed in either condition, both were found to exhibit a similar proportion of altered splicing events, including alternative 3’ splice sites, alternative 5’ splice sites, mutally exclusive exons, and skipped exons with bayes factor > 10 (Figure 6E, Tables S10-S19).
We therefore wanted to further explore this connection between SF3A2 and its role in common variation in RBC traits with SF3B1 and the role it plays in the pathogenesis of MDS. To this end, we utilized a recently developed faithful mouse model harboring the Sf3b1K700E mutation that displays characteristic features of MDS, including an anemia due to impaired erythropoiesis (Obeng et al., 2016). We tested if downregulation of SF3A2 could worsen the already impaired erythropoiesis seen in these animals. Equal numbers of lineage-negative HSPCs were isolated from bone marrow of wild-type and Sf3b1K700E mice and infected with shRNAs targeting SF3A2 and then erythroid differentiation was induced (Figure S6C, D). Consistent with previous reports, we observed that Sf3b1K700E cells show reduced erythroid differentiation and cell growth compared to wild-type cells infected with control non-targeting shRNAs (Figure 6F, G, S6E-G). Downregulation of SF3A2 using two independent shRNAs further worsens the defects in both erythroid differentiation and cell growth observed for Sf3b1K700E cells (Figure 6F, G, S6E, F, G). This data suggests that modulation of SF3A2 could modify the alterations of erythropoiesis observed in the setting of somatic SF3B1 MDS-causal mutations. This form of MDS is characterized by significant variation in the degree of anemia found at the time of presentation (Papaemmanuil et al., 2011). We therefore attempted to examine whether such common genetic variation could contribute to such phenotypic variation. We identified a coding SNP, rs25672, that was in LD with the sentinel SNP at this locus, rs2159213 (r2 = 0.737675 in CEU 1000GENOMES phase 3). Prevalence of the alternate “G” allele (which is associated with the prevalence of the “C” effect allele in the van der Harst et al. locus) has a suggestive correlation with an increase in hemoglobin levels (Figure S6H) that was likely insignificant due to the limited number of patients studied here. Unfortunately, larger cohorts in such a relatively rare disorder could not be identified. However, these findings suggest that the subtle variation noted in populations at the SF3A2 locus may more profoundly cause variation among individuals with an acquired blood disorder, such as MDS, illustrating the value of such a gene-centric study to identify potential disease modifiers.
Discussion
A major challenge in moving from GWAS-nominated variants to function is to identify potential target genes systematically. While many functional follow up approaches focus on causal variants, we reasoned that a gene-centered approach may be complementary to other emerging methods and represent a scalable approach for gaining broad insights into GWAS. To this end, we designed and executed a GWAS-informed high-throughput loss-of-function screen to identify key players in primary human HSPCs undergoing erythroid differentiation. Such dynamic in vitro systems afford a unique window through which to longitudinally screen, enabling unique insight to be gained into inherently non-stationary biological processes like erythropoiesis. The screen identified 77 gene hits at 38 of the original 75 loci used to design the library. Collectively, these hits had strongly amplified essentiality in erythroid cell lines, included a significant proportion of known, genetically-linked “gold standard” erythroid genes, and were enriched for red blood cell trait-associated coding variants orthogonally identified through genetic fine-mapping. From a holistic perspective, the network of interacting gene hits highlighted a number of high-level biological components and pathways important for erythropoiesis, including specific signaling and transcription factors, membrane and structural components, and components involved in mRNA translation.
Functional follow-up on SF3A2 and TFR2, two gene hits identified in the screen, were fruitful in elucidating mechanistic ties between alteration in mRNA splicing and EPO signaling activity, respectively, to observed perturbation of erythroid phenotypes. In addition, our studies suggest that at least SF3A2, and potentially other regulators such as some implicated mRNA translation factors, may be key disease modifiers that alter the impaired erythropoiesis seen in diseases like MDS or Diamond-Blackfan anemia. These outcomes strongly recommend this screening approach as a rapid means to access the genetic mediators underlying human erythroid differentiation, with the potential to much more rapidly derive actionable biology from GWAS studies. Moreover, since shRNA-based loss-of-function screens are readily accessible and offer demonstrated compatibility with primary cell model systems, we believe this approach provides a method that is portable and can be applied across a variety of lines of biological inquiry.
However, it is not a universal solution, and there are certainly a number of considerations that must be kept in mind regarding the extent to which this type of assay can be adopted across other diseases and traits. We acknowledge for it to be useful to a given research question, a suitable system capable of modeling the trait/disease of interest must first exist, and for many cellular systems this is often challenging. Fortunately, this is a shortcoming that will diminish over time as our understanding of human biology and our ability to faithfully recapitulate in vivo microenvironments and processes improves, though this may be a distant prospect for exquisitely complex tissues like the brain or for traits/diseases that involve a larger number of cell types/interactions. Likewise, the use of shRNAs as the vehicle for perturbation carries with it unique challenges, chief among them the proclivity of shRNA to exert confounding off-target effects when compared to CRISPR-based methods. While this is true and unavoidable, the inclusion of appropriate controls, both at the experimental level and in modeling off-target contributors to observed phenotypic effects, provide an effective means to address this issue (Tsherniak et al., 2017). We chose to perform our screen in primary hematopoietic cells and thus were partially limited experimentally to the use of shRNA-based suppressive approaches. Finally, evidence has recently been published that the targets of identified non-coding variants are occasionally not within linkage disequilibrium blocks in the genome (Whalen and Pollard, 2018). This does not necessarily conflict with our results, since we identify hits at only 38 of 75 examined loci and provides an intriguing direction for further work that may elucidate how genetic and epigenomic structural blocks in the human genome can provide complementary information.
Our data shows that gene-centric screens are valuable for GWAS follow-up. They are not limited to red cell traits and may be useful for other human traits/diseases, as has begun to be shown in disease-systems like Type II diabetes (Thomsen et al., 2016). Data from such screens can be integrated with complementary insights gleaned from variant-centric screens. Ultimately this could accelerate our understanding of human hematopoiesis and other biological processes, and aid in the development of applicable therapies.
Author Contributions
V.G.S., D.E.R., S.K.N., S.K.M., J.C.U., G.C. and J.G.D. designed the study. S.K.N performed the pooled shRNA screen experiments in primary cells. G.C. and J.G.D prepared pooled shRNA lentiviral libraries and performed downstream deep sequencing of barcodes. D.E.R. oversaw shRNA library preparation and analysis of the screen. S.K.N., L.M., and G.A. performed all the follow-up experiments. S.K.M., C.A.L, J.C.U., and J.M.E. performed all computational analyses. L.S.L performed RNA sequencing on SF3A2 knockdown samples. M.M. and B.L.E. provided samples from Sf3b1K700E knock-in mice. B.P. and J.P.M. analyzed and provided MDS patient samples. S.K.N., S.K.M., and V.G.S. wrote the manuscript with input from all authors. V.G.S. supervised all experimental and analytic aspects of this work.
Competing Interests
The authors declare no competing interests.
Materials and Methods
Design of the shRNA Library
Ensembl assembly GRCh37p9 was utilized to expand 75 SNPs previously identified in a RBC trait GWAS to include a genomic region in linkage disequilibrium with r2 ≥ 0.8. Each of these regions was then further expanded to the nearest recombination hotspot. All genes in the genome were expanded to include 110 kb upstream and 40 kb downstream of the transcription start and end sites, respectively, to maximize capture of non-coding regulatory interactions based upon previously published observations. Genes with windows calculated in this way found to be overlapping with any of the SNP windows were flagged for inclusion in the screen. In addition, each locus was examined individually, and in cases of gene deserts, unusually proximal recombination hotspots, or other unusual genomic structures, the SNP region was expanded to include additional genes nearby. This resulted in a total of 389 test genes, which were each targeted by 4-7 distinct shRNAs. Also included in the library were shRNAs targeting a set of 8 validated erythroid genes (GATA1, RPL5, RPS19, EPOR, ALAS2, CDAN1, SEC23B, ZFPM1). A pooled library of 2803 TRC clones was produced from the sequence-validated TRC shRNA library (Moffat et al., 2006) and included shRNAs targeting control genes and essential genes.
Pooled shRNA Screening
Mobilized peripheral blood CD34+ cells from three separate donors (7~11 * 106 cells per donor) were differentiated into erythroid cells using a three-stage system that has been previously described (Hu et al., 2013). Cells were cultured using IMDM containing 2% human plasma, 3% human AB serum, 200 μg/ml human holo-transferrin, 3 IU/mL heparin, and 10 mg/mL insulin (base medium). During days 0 to 7, cells were supplemented with IL-3 (1 ng/mL), SCF (10 ng/ml), and EPO (3 IU/ml). On day 2 of this culture, cells were transduced with the pooled lentiviral shRNA library prepared by Broad Institute Genetic Perturbation Platform (1ml of virus per 0.75 * 106 cells) by spinfection at 2000 rpm for 90 minutes with 6 μg/ml polybrene. During days 7 to 13, cells were supplemented with SCF and EPO only. After day 13, cells were supplemented with EPO alone and the holo-transferrin concentration was increased to 1 mg/ml. A minimum of 10 * 106 cells was re-plated at each time point to ensure appropriate library representation and prevent bottlenecks among the infected cells. Cell pellets were made from 20~80 * 106 cells at days 4, 6, 9, 12, 14, and 16. At the conclusion of the pooled screen, genomic DNA (gDNA) was extracted from the cell pellets using NucleoSpin Blood XL-Maxi kit (Clonetech) according to kit specifications. The shRNA-containing region was PCR amplified from the purified gDNA and barcoded using the following conditions: 0.5 μl P5 primer mix (100μM), 10 μl P7 primer mix (5μM), 8 μl dNTP mix, 1x ExTaq buffer, 1.5 μl of ExTaq DNA polymerase (Takara), and up to 10 μg genomic DNA in a total reaction volume of 100 μl. A total of 40~87.5 μg gDNA was used as template from each conditions. Thermal cycler PCR conditions consisted of heating samples to 95 °C for 5 min; 28 cycles of 95 °C for 30 s, 53 °C for 30 s, and 72 °C for 20 s; and 72 °C for 10 min. Equal amounts of samples were then mixed and purified using AMPure XP for PCR purification (Beckman Coulter). Samples were sequenced using a custom sequencing primer using standard Illumina conditions by the Broad Institute Genetic Perturbation Platform. Sequencing reads were deconvolved and hairpin counts were quantified for subsequent analysis by counting against the barcode reference using PoolQ (https://portals.broadinstitute.org/gpp/public/dir/download?dirpath=software&filename=poolq-2.2.0-manual.pdf).
P5 primer
AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT[s]TC TTGTGGAAAGG*A*C*G*A
A mix of P5 primers with stagger regions [s] of different length was used to maintain sequence diversity across the flow-cell.
P7 primer
CAAGCAGAAGACGGCATACGAGATNNNNNNNNGTGACTGGAGTTCAGACGTGTGCTCTTC CGATCTTCTACTATTCTTTCCCCTGCA*C*T*G*T
Independently barcoded P7 primers was used for each condition.
NNNNNNNN – barcode region
Analysis of the shRNA Screen
Three separate donor primary CD34+ cells populations were run as replicates in the shRNA screen. A pseudocount of 1 was added to all shRNA-encoding DNA count totals and these counts were subsequently normalized to counts per million (CPM) and log2 transformed. A linear mixed model was constructed to fit fixed effects for each gene (g) using the log2 fold change from initial hairpin counts as the response variable (y). A random effect was included to capture variations in efficacy and off-target effects for each shRNA (h) used to target a given gene. The resulting model, y ~ g + (0 + h|g), was fit in R-3.4 using the lme4 package. Genes hits were called from the set of genes with β coefficient effect size > 0.1 and the Wald chi-square test adjusted q value < 0.1. Enrichment of erythroid essential genes within the hit set was calculated by running 1 million permutations against the distribution of K562 essentiality for all genes included in the library, panels of genes nominated by sets of significant GWAS-associated lipid trait SNPs (Willer et al., 2013), and against all genes in the genome (Ensembl GRCh37p9). Enrichment for identification of the included 5 “gold standard” genes and for red blood cell trait-associated coding variants were each accomplished using identical permutation schemes. Expression of the hit genes in various cell states/stages of differentiation were derived from the cited datasets and permuted across all unique stages to determine stage-specific enrichment. The interaction network surrounding the 77 hits identified in the screen was generated in the latest version of STRING (10.5) and filtered for the purposes of display to only those nodes with at least one edge to another node among the hits.
RNA-Seq
Stage matched CD71+/CD235a+ cells derived from CD34+ HSPCs infected with SF3A2 sh3, sh4 and shLUC were FACS sorted at day 8 of erythroid differentiation. RNA was isolated using a RNAqueous Micro kit (Invitrogen) according to the manufacturer’s instructions. DNase digestion was performed before RNA was quantified using a Qubit RNA HS Assay kit (Invitrogen). 1-10 ng of RNA were used as input to a modified SMART-seq2 (Picelli et al., 2014) protocol and after reverse transcription, 8-9 cycles of PCR were used to amplify transcriptome library. Quality of whole transcriptome libraries was validated using a High Sensitivity DNA Chip run on a Bioanalyzer 2100 system (Agilent), followed by library preparation using the Nextera XT kit (Illumina) and custom index primers according to the manufacturer’s instructions. Final libraries were quantified using a Qubit dsDNA HS Assay kit (Invitrogen) and a high sensitivity DNA chip run on a Bioanalyzer 2100 system (Agilent). All libraries were sequenced using Nextseq High Output Cartridge kits and a Nextseq 500 sequencer (Illumina). Libraries were sequenced using 2×38bp paired end reads.
RNA-seq Differential Expression Analysis
For differential expression analysis, paired end sequencing reads from our SF3A2 shRNA knockdown experiments and obtained from the SF3B1 mutant datasets (Obeng et al., 2016) were quantified using Salmon version 0.11.1 (Patro et al., 2017) with default parameters and an index constructed from Gencode annotations version 28. Differential expression of quantified counts were calculated using DESeq2 (Love et al., 2014) in R-3.4. Enrichment for functions and components of the cell among the differentially expressed gene sets were quantified using GOrilla (Eden et al., 2007; Eden et al., 2009).
RNA-seq Differential Splicing Analysis
Paired end sequencing reads from our SF3A2 shRNA knockdown experiments and obtained from the cited SF3B1 mutant datasets were aligned using STAR version 2.5.2 in two-pass mode. Differential splicing was quantified using MISO version 0.5.4 in Python 2.7 using the instructions and annotation files provided with the package (Katz et al., 2010).
Analysis of Hemoglobin Levels for MDS Patients with or without SF3A2 Mutations
Genotyped MDS patient hemoglobin level measurements were obtained from the laboratory of J. Maciejewski. 1000GENOMES phase 3 data was used to find a SNP encoded in whole-exome sequencing data (rs25672) in high LD (r2 = 0.737675) with the SF3A2-associated sentinel SNP (rs2159213). An ordinary least squares linear regression was used to fit the patient hemoglobin levels to the number of SF3A2 minor alleles present in each patient (log likelihood ratio test p = 0.140).
Phosphorylated STAT5 assessment with Intracellular Flow Cytometry
UT-7/EPO cells were cultured in DMEM medium supplemented with 10% Fetal Bovine Serum and 2 U/mL EPO. 5 days post-infection with TFR2 shRNAs, UT-7/EPO cells were cytokine starved overnight. On the next day, cells were treated with EPO in a dose dependent manner ((0 U/mL, 0.002 U/mL, 0.02 U/mL, 0.2 U/mL, 2 U/mL, 20 U/mL and 200 U/mL) and incubated 37 °C for 30 min. Alternatively the cells were treated with 2U/ml EPO in a time dependent manner (15, 30, 60, 120,180 min). Treated cells were gently mixed with pre-warmed Fixation Buffer (BD Bioscience) at 37°C for 10 min to fix cells. To permeabilize cells for intracellular staining, cells were resuspended in pre-chilled Perm Buffer III (BD Bioscience) for 30 min at 4°C. After three washes with 3% FBS in PBS, samples were stained either with Alexa Fluor-647 Mouse Anti-phospho-STAT5 (pY694; 1:20 dilution) for 1 hr in the dark at room temperature. A BD Accuri C6 Cytometer (BD Bioscience) was used to acquire mean fluorescent intensity (MFI) of phospho-STAT5-Alexa Fluor 647. The MFI of phospho-STAT5-Alexa Fluor 647 of gated single cells was calculated using FlowJo (version 10.0.8r1). Unstimulated UT7/EPO cells were used as a negative control.
May-Grünwald-Giemsa staining
Approximately 50,000 – 200,000 cells were harvested, washed once at 300 x g for 5 min, resuspended in 200 μL FACS buffer and spun onto poly-L-lysine coated glass slides (Sigma Aldrich) with a Shandon 4 (Thermo Fisher) cytocentrifuge at 300 rpm for 4 min. Visibly dry slides were stained with May-Grünwald solution for 5 min, rinsed 4 times for each 30 s in H2O, transferred to Giemsa solution for 15 min and washed as described above. Slides were dried overnight and mounted with coverslip. All images were taken with AxioVision software (Zeiss) at 100 x magnification.
Mouse erythroid differentiation culture
Bone marrow cells were isolated from SF3B1K700E +/- mice and littermate controls were lineage depleted using Lineage Cell Depletion Kit, mouse (Miltenyi Biotech) according to manufacturer’s protocols. Lineage negative cells were immediately transduced with lentiviral shRNAs targeting SF3A2 or controls (MOI -90) by spinfection at 2000rpm for 90 min. The cells were cultured in erythroid maintenance medium (StemSpan-SFEM; StemCell Technologies) supplemented with 100 ng/mL recombinant mouse stem cell factor (SCF) (R&D Systems), 40 ng/mL recombinant mouse IGF1 (R&D Systems), 100 nM dexamethasone (Sigma), and 2 U/mL erythropoietin (Amgen) and cultured at 37°C for 36 hours. Following this the cells were cultured for another 48 hours in erythroid differentiation medium (Iscove modified Dulbecco’s medium containing 15% (vol/vol) FBS (Stemcell), 1% detoxified BSA (Stemcell), 500 μg/mL holo-transferrin (Sigma-Aldrich), 0.5 U/mL Epoetin (Epo; Amgen), 10 μg/mL recombinant human insulin (Sigma-Aldrich), and 2 mM L-glutamine (Invitrogen)) at 37 °C.
Flow cytometry analyses and antibodies
All flow cytometry data was acquired using either using LSR II SORP or LSR Fortessa flow cytometers (BD Biosciences). All staining was carried out in FACS buffer (2% FBS in PBS) for 30 minutes on ice unless otherwise described. The following antibodies were used anti-human CD235a-APC (eBioscience, Clone HIR2), anti-human CD71-FITC (eBioscience, Clone OKT9), anti-human CD71-PEcy7 (eBioscience, Clone OKT9), ant-human CD49d-PE (Miltenyi, Clone MZ18-24A9), anti-human CD41a-PE (eBioscience, Clone HIP8), anti-human CD11b-PE (eBioscience, Clone ICRF44), anti-mouse Ter119-APC (eBioscience, Clone TER119), anti-mouse CD71-PE (eBioscience, Clone R17217) and Alexa Fluor-647 anti-phospho STAT5 (pY694) (BD Bioscience Cat#: 612599). Hoechst 33342 (Life Technologies, H1399) was used to visualize nuclei.
shRNA sequences
The following lentiviral shRNA constructs were generated in Polymerase III based shRNA backbone pLKO.1-puro (Sigma Aldrich).
shLUC
5’-CCGGCGCTGAGTACTTCGAAATGTCCTCGAGGACATTTCGAAGTACTCAGCGTTTTTG-3’
TFR2 sh1
5’-CCGGGCCAGATCACTACGTTGTCATCTCGAGATGACAACGTAGTGATCTGGCTTTTTG-3
TFR2 sh2
5’-CCGGCAACAACATCTTCGGCTGCATCTCGAGATGCAGCCGAAGATGTTGTTGTTTTTG-3’
SF3A2 sh1 (human)
5’-CCGGCTACGAGACCATTGCCTTCAACTCGAGTTGAAGGCAATGGTCTCGTAGTTTTT-3
SF3A2 sh2 (human)
5’-CCGGCCTGGGCTCCTATGAATGCAACTCGAGTTGCATTCATAGGAGCCCAGGTTTTT-3’
SF3A2 sh3 (human)
5’-CCGGCAAAGTGACCAAGCAGAGAGACTCGAGTCTCTCTGCTTGGTCACTTTGTTTTT-3
SF3A2 sh4 (human)
5’-CCGGACATCAACAAGGACCCGTACTCTCGAGAGTACGGGTCCTTGTTGATGTTTTTT-3’
The following lentiviral shRNA constructs were generated in Polymerase II based mir30 shRNA backbone developed in the lab SFFV-Venus-mir30 shRNA backbone.
shNT(non-targeting)
5’_TGCTGTTGACAGTGAGCGATCTCGCTTGGGCGAGAGTAAGTAGTGAAGCCACAGATGTA CTTACTCTCGCCCAAGCGAGAGTGCCTACTGCCTCGGA_3’
Sf3a2 sh1 (mouse)
5’_TGCTGTTGACAGTGAGCGCGGAGGTGAAGAAGTTTGTGAATAGTGAAGCCACAGATGTA TTCACAAACTTCTTCACCTCCATGCCTACTGCCTCGGA_3’
Sf3a2 sh2 (mouse)
5’_TGCTGTTGACAGTGAGCGACCACCGTTTCATGTCTGCTTATAGTGAAGCCACAGATGTAT AAGCAGACATGAAACGGTGGCTGCCTACTGCCTCGGA_3’
Sf3a2 sh3 (mouse)
5’_TGCTGTTGACAGTGAGCGATCCTGCCTTGAGCCTATTAAATAGTGAAGCCACAGATGTAT TTAATAGGCTCAAGGCAGGACTGCCTACTGCCTCGGA_3’
Sf3a2 sh4 (mouse)
5’_TGCTGTTGACAGTGAGCGACCACTGGAACAGAGAAACCAATAGTGAAGCCACAGATGTA TTGGTTTCTCTGTTCCAGTGGGTGCCTACTGCCTCGGA_3’
Sf3a2 sh5 (mouse)
5’_TGCTGTTGACAGTGAGCGATGGAGGTGAAGAAGTTTGTGATAGTGAAGCCACAGATGTA TCACAAACTTCTTCACCTCCACTGCCTACTGCCTCGGA_3’
qPCR primers
TFR2 Fwd: 5’-ATCCTTCCCTCTTCCCTCCC-3’
TFR2 Rev: 5’-CCATCCAGCCACATGGTTCT-3
SF3A2 Fwd: 5’-CCTGAGAAGGTCAAGGTGGA-3’
SF3A2 Rev: 5’-CTCCGAGTCTCTCTGCTTGG-3’
Western Blot antibodies
Anti-GAPDH (Santa Cruz Biotechnology, sc-32233); anti-TFR2 (Santa Cruz Biotechnology, scsc-32271); anti-SF3A2 (Santa Cruz Biotechnology, sc-390444)
Acknowledgments
We thank members of the Sankaran laboratory for valuable comments and suggestions on these studies. This work was supported by the National Institutes of Health grants R01 DK103794 and R33 HL120791, as well as the New York Stem Cell Foundation (to V.G.S.). V.G.S. is a New York Stem Cell Foundation-Robertson Investigator.