1. Genetics and Genomics
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Gene-centric functional dissection of human genetic variation uncovers regulators of hematopoiesis

  1. Satish K Nandakumar
  2. Sean K McFarland
  3. Laura M Mateyka
  4. Caleb A Lareau
  5. Jacob C Ulirsch
  6. Leif S Ludwig
  7. Gaurav Agarwal
  8. Jesse M Engreitz
  9. Bartlomiej Przychodzen
  10. Marie McConkey
  11. Glenn S Cowley
  12. John G Doench
  13. Jaroslaw P Maciejewski
  14. Benjamin L Ebert
  15. David E Root
  16. Vijay G Sankaran  Is a corresponding author
  1. Harvard Medical School, United States
  2. Broad Institute of MIT and Harvard, United States
  3. Ruprecht-Karls-University Heidelberg, Germany
  4. University of Oxford, United Kingdom
  5. Harvard Stem Cell Institute, United States
  6. Harvard University, United States
  7. Cleveland Clinic, United States
  8. Brigham and Women’s Hospital, United States
  9. Dana-Farber Cancer Institute, United States
  10. Howard Hughes Medical Institute, United States
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Cite this article as: eLife 2019;8:e44080 doi: 10.7554/eLife.44080

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.

https://doi.org/10.7554/eLife.44080.001

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 (Canver et al., 2015; Chen et al., 2015; 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., 2017; International Inflammatory Bowel Disease Genetics Consortium et al., 2017; Ulirsch et al., 2019). 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 screen for potential candidate gene mediators and identify networks of biological actors underlying trait-associated variation. We additionally illustrate the value of such screens to uncover previously unappreciated 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 1—figure supplement 1A). Importantly, these 75 loci have been robustly replicated in more recently reported association studies performed on larger cohorts and thus represent important targets for perturbation studies (Astle et al., 2016; Ulirsch et al., 2019). We endeavored to select candidate 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 1—figure supplement 1B), then further to the nearest genomic recombination hotspot. Based upon insights from previous expression quantitative trait locus (eQTL) studies (Montgomery and Dermitzakis, 2011; International Inflammatory Bowel Disease Genetics Constortium 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 1—figure supplement 1C). This approach may on occasion miss genes targeted by a trait-associated regulatory element. For example, our approach would miss long-range interactions, as is observed at the FTO locus with IRX3/IRX5 (Claussnitzer et al., 2015; Smemo et al., 2014). It is becoming increasingly clear that LD and related metrics will only nominate a fraction of potential regulatory targets (Whalen and Pollard, 2019). However, there still exists a nontrivial amount of valid targets within reach of proximity LD approaches, especially when the calculation of such windows are extended to reach the nearest recombination hotspot, suggesting that our approach would capture many candidate target genes.

Figure 1 with 3 supplements see all
Design and Execution of an shRNA Screen Using Blood Cell Trait GWAS Hits to Identify Genetic Actors in Erythropoiesis.

(A) Overview of shRNA library design.75 loci associated with red blood cell traits (van der Harst et al., 2012) were used as the basis to calculate 75 genomic windows of LD 0.8 or greater from the sentinel SNP. Genes with a start site within 110 kb or end site within 40 kb of the LD-defined genomic windows were chosen as candidates to target in the screen. (B) Compositional makeup of the library, depicted as number of genes and number of hairpins for each of the four included subcategories; GWAS-nominated genes, erythroid genes, essential genes, and negative control genes (Figure 1—source data 2). (C) Primary CD34+hematopoietic stem and progenitor cells (HSPCs) isolated from three independent donors were cultured for a period of 16 days in erythroid differentiation conditions. At day 2, cells were infected with the shRNA library, and the abundances of each shRNA were measured at days 4, 6, 9, 12, 14, and 16 using deep sequencing.

https://doi.org/10.7554/eLife.44080.002
Figure 1—source data 1

Table containing annotations and information for the 75 SNPs used to seed the shRNA library.

https://doi.org/10.7554/eLife.44080.006
Figure 1—source data 2

Table containing annotations and information for all hairpins, as well as shRNA counts for each time point and replicate.

https://doi.org/10.7554/eLife.44080.007

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 and our results have been consistent with orthogonal CRISPR based approaches (Giani et al., 2016). Second, shRNA libraries can be much more efficiently packaged into lentiviruses and delivered to primary HSPCs compared to alternative CRISPR/Cas9-based guide RNA libraries (Ting et al., 2018). We observe that all in one CRISPR or CRISPRi lentiviral constructs that are ideal for primary HSPC screens produce low titer viruses and require very high multiplicities of infection that can be challenging to achieve for a pooled functional screen (Figure 1—figure supplement 2A,B). Third, the shRNA approach 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 (Figure 1—figure supplement 2C) (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 three 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 GWAS-nominated gene was targeted with 5–7 distinct shRNAs (Figure 1—figure supplement 3A). The set of control shRNAs encompassed essential housekeeping genes as positive controls, negative controls in the form of luciferase and other genes not expressed in humans, and a well-established set of genes known to be important for erythropoiesis (erythroid controls) (Figure 1B). Using lentiviral libraries with defined titers, we achieved an infectivity of 35–50%, which provides a good tradeoff between reasonably high infection while minimizing the possibility of multiple integrations per cell that can lead to combinatorial phenotypes. 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 semi-synchronous erythroid differentiation method where differentiation blockade reduces cell numbers either through cell death or through a failure of proliferation (Giani et al., 2016; Hu et al., 2013). 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 six 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, Figure 1—figure supplement 3B).

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 five logcounts 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, Figure 2—figure supplement 1).

Figure 2 with 2 supplements see all
Summary Characterization of shRNA Screen Outcomes.

(A) Kernel density plot showing library representation as log2 shRNA CPM across all hairpins. (B) shRNA abundance log2 fold changes from day 4 to day 16. Represented values are the mean of hairpin abundance log2 fold changes across hairpins for each gene and two standard deviations. (C) Kernel density plots representing the day 4 to day 16 log2 fold changes of hairpin abundances for each of the subcategories of the library, including GWAS-nominated genes, known erythroid essential genes, essential genes to cell viability, and orthogonal genes serving as negative controls. (D) Violin plot of day 4 and day 16 log2 CPM for known actors GATA1 and RPS19 and negative controls LacZ and luciferase. (E) Log2 hairpin counts averaged for known actors GATA1 and RPS19 as well as negative controls LacZ and luciferase across the course of the experiment. Gray lines depict the universe of all other gene traces in the library for context.

https://doi.org/10.7554/eLife.44080.008

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 2B). The various controls included in the library behaved as expected. Hairpins targeting genes with known biological roles in 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 (Figure 1—source data 2) 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 2—figure supplement 2).

Statistical modeling of gene effects and accounting for confounders in the shRNA screen

The resulting 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 pooled together the observations for each of the three donor replicates and 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, Figure 3—figure supplement 1A). These candidate genes were found to be distributed across all 6 of the originally annotated RBC GWAS traits (Figure 3—figure supplement 1B), and hairpins targeting them showed strong internal consistency (Figure 3—figure supplement 1C).

Figure 3 with 3 supplements see all
Statistical Modeling of Gene Effect Accounting for Off-target shRNA Confounders.

(A) Bar graph showing the 38 of 75 loci in the screen with at least one corresponding statistically significant (FDR < 0.1, β >0.1) gene effect causing either a positive or negative log2 fold change in shRNA abundance.Statistical model output for each gene in screen available in Figure 3—source data 1. (B) Kernel density plot showing the expected distributions of K562 essentiality scores using permuted gene hit sets from the library. (C) Hairpin rank sums for permuted sets of 5 genes. The red line indicates the enriched rank sums for 5 ‘gold standard’ genes included in the library, CCND3, SH2B3, MYB, KIT, and RBM38, for each which a genetic basis of action has already been established. (D) Permuted distribution of % inclusion of predicted coding variants among the set of identified hits. (E) Heat map depicting strength of expression (as z scores within each gene) for each of the 77 identified hit genes across hematopoietic lineages (top) and throughout the specific stages of adult erythropoiesis (bottom). Purple boxes highlight the cell types that were enriched for expression of hit genes. (F) Calculated enrichment of the identified hit genes for expression across hematopoietic lineages (top) and throughout the specific stages of adult erythropoiesis (bottom). In both cases, cellular states corresponding to those along the erythropoietic lineage had elevated probability of expressing genes from the hit set as compared to other genes from the library.

https://doi.org/10.7554/eLife.44080.011
Figure 3—source data 1

Table containing the R model output for each gene.

https://doi.org/10.7554/eLife.44080.015

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 3—figure supplement 2A), 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 (Global Lipids Genetics Consortium et al., 2013) (Figure 3—figure supplement 2B–D).

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 (Sankaran et al., 2012; Ulirsch et al., 2019), SH2B3 (Giani et al., 2016), MYB (Galarneau et al., 2010; Sankaran et al., 2013; Sankaran et al., 2011), KIT (Jing et al., 2008; Ulirsch et al., 2019), 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 (Ulirsch et al., 2019). Collectively, these coding variants were found to be 75% missense, 19% synonymous, and 5% frameshift. 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 3—figure supplement 3). For each cellular context, gene expression values were z-score normalized for each gene targeted in the screen. Enrichment was tested through permutation by using the sum of expression z-scores at each stage for the called hits as the benchmark, and comparing these to sums derived from expression values from a matching number of genes randomly drawn from the wider screen. In the more holistic hematopoiesis dataset, common myeloid progenitors (CMPs) and megakaryocyte-erythroid progenitors (MEPs) were significantly enriched for hit gene expression (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; DBA Group of Société d'Hématologie et d'Immunologie Pédiatrique-SHIP et al., 2012) as well as late genes like SLC4A1 and ANK1 (Bennett and Stenbuck, 1979; Peters et al., 1996). To examine how our results compare to target gene identification through eQTL-based approaches, we also examined the whole blood eQTL dataset form the Genotype-Tissue Expression (GTEx) Project, finding that none of the 77 shRNA screen hits emerged using eQTLs located within the LD blocks of the original 75 sentinel SNPs (GTEx Consortium, 2015). This is not entirely surprising given that the shRNA screen was performed on differentiating erythroid progenitors which are essentially not present in whole blood, so one would expect to miss cell type-specific effects or eQTLs that act in early progenitor populations. Taken together, these results show that this functional gene-centric screen can identify putative causal genes underlying RBC-trait GWAS hits orthogonal to those that would be found with more conventional eQTL-based methods, and 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 at 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 for erythropoiesis

By screening all loci and genes at once, our approach afforded us the immediate value of examining associations between hits in a holistic fashion, unearthing both familiar and more novel gene cassettes that play a role in erythropoiesis (Boyle et al., 2017). Using STRING interaction network analyses (version 10.5) (Szklarczyk et al., 2017), we used empirical, database-curated, co-expression, genomic proximity, and text-mined evidence to identify underlying networks between hits in the screen. These networks highlighted a number of interacting biological processes of both known and previously unappreciated importance to erythropoiesis (Figure 4), including cell signaling and transcription, cytoskeletal and membrane structure and function, and mRNA translation. 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 (Sankaran et al., 2012; Ulirsch et al., 2019).

Analysis of Interactions Among Members of the Hit Set Identifies Signaling/Transcription, Membrane, and mRNA Translation-Related Subnetworks Important to Erythropoiesis.

STRING interaction network analysis identifies signaling/transcription, membrane, and mRNA translation-related subnetworks important to erythropoiesis embedded in the genes identified in the screen hit set. Edges connecting the network are color-coded according to the evidence supporting the interaction. In STRING, this evidence can derive from empirical determination, curation in a database, co-expression of the respective gene nodes, genomic proximity, and text-mining of published literature.

https://doi.org/10.7554/eLife.44080.016

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; DBA Group of Société d'Hématologie et d'Immunologie Pédiatrique-SHIP 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 suggested 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 (Forejtnikovà 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 and E). Downregulation of TFR2 also improved the later stages of erythroid differentiation/maturation, as observed by loss of cell surface marker CD49d at day 15 of culture, an increased rate of enucleation, and through assessment of cell morphology (Figure 5D and Figure 5—figure supplement 1A and B). Previous studies have reported the isolation of TFR2 as a component of the erythropoietin (EPO) receptor complex (Forejtnikovà et al., 2010). To test if downregulation of TFR2 can result in increased EPO signaling Kim et al. (2017), we measured EPO-dependent STAT5 phosphorylation after TFR2 knockdown in UT7/EPO cells (Figure 5—figure supplement 1C). 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 5—figure supplement 1D). 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).

Figure 5 with 1 supplement see all
Transferrin receptor two is a Negative Regulator of Human Erythropoiesis.

(A) Quantitative RT-PCR and (B) Western blot showing the expression of TFR2 in human CD34+ cells five days post-infection with the respective lentiviral shRNAs targeting TFR2 (TFR2 sh1 and sh2) and a control luciferase gene (shLUC). (C) Representative FACS plots of erythroid cell surface markers CD71 (transferrin receptor) and CD235a (Glycophorin A) expression at various time points during erythroid differentiation. Percentages in each quadrant are represented as mean and standard deviation of 3 independent experiments (D) Hoechst staining showing more enucleated cells after TFR2 knockdown at day 21 of erythroid culture. (E) Representative histogram plots showing increased expression of CD235a (Glycophorin A) after TFR2 knockdown (F) Enhanced pSTAT5 response after TFR2 knockdown in UT7/EPO cells.

https://doi.org/10.7554/eLife.44080.017

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 6—figure supplement 1A). During erythroid differentiation, we observed a reduction in GFP-expressing cells comparable to the decreased cell numbers seen with the puromycin resistant constructs (Figure 6—figure supplement 1A). 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 (megakaryocytic) (Figure 6—figure supplement 1B).

Figure 6 with 2 supplements see all
SF3A2 is a Key regulator of Human Erythropoiesis and Modulates Erythropoiesis Defects in a Murine Model of MDS.

(A) Quantitative RT-PCR and (B) Western blot showing the expression of SF3A2 in human CD34+ cells five days post-infection with the respective lentiviral shRNAs targeting SF3A2 (sh1-4) and a control luciferase gene (shLUC). (C) Growth curves showing that downregulation of SF3A2 results in reduced total cell numbers during erythroid differentiation from three independent experiments. (D) Representative FACS plots of erythroid cell surface markers CD71 (transferrin receptor) and CD235a (Glycophorin A) expression at various time points during erythroid differentiation. Percentages in each quadrant are represented as mean and standard deviation of three independent experiments (E) Altered splicing events identified by RNA-Seq analysis of stage matched erythroid cells (shSF3A2 vs. shLUC). Overlapping changes observed in SF3B1 mutant BM cells from MDS patients (Obeng et al) (Figure 6—source data 5 and 6). Differentially expressed genes and pathway analysis available in Figure 6—source data 14. (F) Lineage negative bone marrow cells from wildtype (WT) and Sf3b1K700E mice were infected with shRNAs targeting murine Sf3a2 gene co-expressing a reporter GFP gene. Percentage of Ter119+ CD71+ erythroid cells within the GFP compartment after 48 hr in erythroid differentiation. (G) Total cell numbers of GFP+ erythroid cells after 48 hr in erythroid differentiation.

https://doi.org/10.7554/eLife.44080.019
Figure 6—source data 1

Table containing the DESeq2 output for differentially expressed genes in cells undergoing SF3A2 knockdown or control shRNA treatment.

https://doi.org/10.7554/eLife.44080.022
Figure 6—source data 2

Table containing the DESeq2 output for differentially expressed genes in MDS patients with and without mutations in SF3B1.

https://doi.org/10.7554/eLife.44080.023
Figure 6—source data 3

Tables containing the GO component (Table 1) and function (Table 2) enrichments calculated using GOrilla for cells undergoing SF3A2 knockdown or control shRNA treatment.

https://doi.org/10.7554/eLife.44080.024
Figure 6—source data 4

Tables containing the GO component (Table 1) and function (Table 2) enrichments calculated using GOrilla for MDS patient samples with and without mutations in SF3B1.

https://doi.org/10.7554/eLife.44080.025
Figure 6—source data 5

Tables containing the differential splicing analysis for cells undergoing SF3A2 knockdown or control shRNA treatment.

Categories of splice mutations presented in each table are alternative 3’ splice sites, alternative 5’ splice sites, mutually exclusive exons, retrained introns, and skipped exons, respectively.

https://doi.org/10.7554/eLife.44080.026
Figure 6—source data 6

Tables containing the differential splicing analysis for MDS patient patient samples with and without mutations in SF3B1.

Categories of splice mutations presented in each table are alternative 3’ splice sites, alternative 5’ splice sites, mutually exclusive exons, retrained introns, and skipped exons, respectively.

https://doi.org/10.7554/eLife.44080.027

To identify the molecular mechanisms underlying the reduced differentiation of erythroid cells, we sorted stage-matched CD71+/CD235a+ cells and performed RNA-Seq analysis. We also ran this analysis in parallel for data from hematopoietic progenitors of patients with myelodysplastic syndrome (MDS), a disorder well-known for significant impairments 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 (Figure 6—source data 3 and 4). 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, mutually exclusive exons, and skipped exons (Figure 6E).

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 6—figure supplement 2A,B). 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, Figure 6—figure supplement 2C–E). 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, Figure 6—figure supplement 2C–E). This data suggest 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 (Chronic Myeloid Disorders Working Group of the International Cancer Genome Consortium 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, in LD with the sentinel SNP at the locus, rs2159213 (r2 = 0.737675 in CEU 1000 Genomes phase 3), in which SF3A2 was the only gene identified by the linear mixed model as a hit. Prevalence of the alternate ‘G’ allele (which corresponds to the prevalence of the ‘C’ effect for SF3A2) is correlated with an increase in hemoglobin levels () 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 rs2159213 locus containing SF3A2 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 insights 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. It is interesting to note that the hits identified by our screen did not overlap those that would be identified with eQTLs from whole blood, which emphasizes how studies of variation in developing hematopoietic cells may not be accurately reflected by studies of circulating blood cells. It does, however, lend credence to the notion that there are complementary insights to be gained through these differing methods.

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 further exploration of this approach as a rapid means to screen for genes underlying human erythroid differentiation, with the potential to connect back and explain the phenotypic links in 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, 2019). 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 show 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 diseases like type 2 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.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
Biological sample (Homo sapiens)CD34 + mobilized peripheral bloodFred Hutchinson Cancer Research Center
Cell line (Homo sapiens)UT-7/EPONARRID:CVCL_5202maintained in Sankaran laboratory
Cell line (Mus musculus)MELNAmaintained in Sankaran laboratory
Genetic reagent (Mus musculus)Sf3b1K700EObeng et al., 2016Dr. Benjamin L. Ebert (Brigham Women's Hospital, Boston MA)
Recombinant DNA reagent (lentiviral shRNA)PLKO.1-Puro (plasmid)Sigma-AldrichRRID
:Addgene_10878
Pol III based shRNA backbone
Recombinant DNA reagent (lentiviral shRNA)PLKO-GFP (plasmid)this paperGFP version of pLKO.1-Puro
Recombinant DNA reagent (lentiviral shRNA)SFFV-Venus-mir30 shRNA (plasmid)this paperPol II based shRNA backbone
Antibodymouse monoclonal anti-human CD235a-APCThermo Fisher ScientificCat#: 17-9987-42; RRID:AB_2043823FACS (5 ul per test)
Antibodymouse monoclonal anti-human CD71-FITCThermo Fisher ScientificCat#: 11-0719-42; RRID:AB_1724093FACS (5 ul per test)
Antibodymouse monoclonal anti-human CD71-PEcy7Thermo Fisher ScientificCat#: 25-0719-42; RRID:AB_2573366FACS (5 ul per test)
Antibodymouse monoclonal ant-human CD49d-PEMiltenyi BiotecCat#: 130-093-282; RRID:AB_1036224FACS (10 ul per test)
Antibodymouse monoclonal anti-human CD41a-PEThermo Fisher ScientificCat#: 12-0419-42; RRID:AB_10870785FACS (5 ul per test)
Antibodymouse monoclonal anti-human CD11b-PEThermo Fisher ScientificCat#: 12-0118-42; RRID:AB_2043799FACS (5 ul per test)
AntibodyRat monoclonal anti-mouse Ter119-APCThermo Fisher ScientificCat#: 17-5921-82; RRID:AB_469473FACS (0.25 ug/test)
AntibodyRat monoclonal anti-mouse CD71-PEThermo Fisher ScientificCat#: 12-0711-82; RRID:AB_465740FACS (0.5 ug/test)
Antibodymouse monoclonal anti-phospho STAT5 Alexa Fluor-647BD BioscienceCat#: 612599; RRID:AB_399882FACS (1:20)
Antibodymouse monoclonal anti-GAPDHSanta Cruz Biotechnologysc-32233;
RRID:AB_627679
Western (1:20,000)
Antibodymouse monoclonal anti-TFR2Santa Cruz Biotechnologysc-32271; RRID:AB_628395Western (1:200)
Antibodymouse monoclonal anti-SF3A2Santa Cruz Biotechnologysc-390444Western (1:1000)
Sequence-based reagentshLUCSigma-AldrichTRCN00000722595’- CGCTGAGTACTTCGAAATGTC-3’
Sequence-based
reagent
TFR2 sh1 (human)Sigma-AldrichTRCN00000636285’-GCCAGATCACTACGTTGTCAT-3’
Sequence-based reagentTFR2 sh2 (human)Sigma-AldrichTRCN00000636325-CAACAACATCTTCGGCTGCAT-3’
Sequence-based reagentSF3A2 sh1 (human)Sigma-AldrichTRCN00000000605’-CTACGAGACCATTGCCTTCAA-3’
Sequence-based reagentSF3A2 sh2 (human)Sigma-AldrichTRCN00000000615’-CCTGGGCTCCTATGAATGCAA-3’
Sequence-based reagentSF3A2 sh3 (human)Sigma-AldrichTRCN00000000625’-CAAAGTGACCAAGCAGAGAGA-3’
Sequence-based reagentSF3A2 sh4 (human)Sigma-AldrichTRCN00000000635’-ACATCAACAAGGACCCGTACT-3’
Commercial assay or kitRNeasy Mini KitQIAGENCat#: 74104
Commercial assay or kitiScript cDNA synthesis KitBio-RadCat#: 1708891
Commercial assay or kitiQ SYBR Green SupermixBio-RadCat#: 170–8882
Commercial assay or kitNucleoSpin Blood XL-Maxi kitClonetchCat#: 740950.1
Commercial assay or kitLineage Cell Depletion Kit (mouse)MiltenyiCat#: 130-090-858
Commercial assay or kitNextera XT DNA Library Preparation KitIlluminaCat#: FC-131–1096
Commercial assay or kitNextSeq 500/550 High Output Kit v2.5 (75 Cycles)IlluminaCat#: 20024906
Commercial assay or kitBioanalyzer High Sensitivity DNA AnalysisAgilentCat#: 5067–4626
Commercial assay or kitAgencourt AMPure XPBeckman-CoulterCat#: A63881
Commercial assay or kitTaKaRa Ex TaqDNA PolymeraseTakaraCat#: RR001B
Commercial assay or kitQubit dsDNA HS Assay KitThermo FisherCat#: Q32854
Chemical compound, drugHuman Holo-TransferrinSigma AldrichCat#: T0665-1G
Peptide, recombinant proteinHumulin R (insulin)LillyNDC 0002-8215-01
Peptide, recombinant proteinHeparinHospiraNDC 00409-2720-01
Peptide, recombinant proteinEpogen (recombinant erythropoietin)AmgenNDC 55513-267-10
Peptide, recombinant proteinRecombinant human stem cell factor (SCF)PeprotechCat#: 300–07
Peptide, recombinant proteinRecombinant human interleukin-3 (IL-3)PeprotechCat#: 200–03
Peptide, recombinant proteinRecombinant mousestem cell factor (SCF)R&D systemsCat# 455-MC-010
Peptide, recombinant proteinrecombinant mouse Insulin like Growth Factor 1 (IGF1)R&D systemsCat# 791 MG-050
Chemical compound, drugHoechst 33342Life TechnologiesCat#: H1399FACS (1:1000)
Chemical compound, drugFixation BufferBD BioscienceCat#: 554655
Chemical compound, drugPerm Buffer IIIBD BioscienceCat#: 558050
Chemical compound, drugMay-Grünwald StainSigma-AldrichCat#: MG500
Chemical compound, drugGiemsa StainSigma-AldrichCat#: GS500
Software, algorithmSTARDobin et al., 2013RRID:SCR_015899
Software, algorithmMISOKatz et al., 2010RRID:SCR_003124
Software, algorithmRThe R FoundationRRID:SCR_001905
Software, algorithmSalmonPatro et al., 2017RRID:SCR_017036
Software, algorithmGOrillaEden et al., 2009RRID:SCR_006848
Software, algorithmVEPMcLaren et al., 2016RRID:SCR_007931
Software, algorithmFlowJo version 10FlowJoRRID:SCR_008520
Software, algorithmGraphPad Prism 7GraphPad Software IncRRID:SCR_002798
Software, algorithmPython 2, 3Python Software FoundationRRID:SCR_008394
Software, algorithmPLINKChang et al., 2015RRID:SCR_001757
Software, algorithmPoolQBroad Institutehttps://portals.broadinstitute.org/gpp/public/software/poolq

Design of the shRNA library

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PLINK version 1.9 and 1000 genomes phase one data were 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 Ensembl assembly GRCh37 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

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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 (1 ml of virus per 0.75 * 106 cells) by spinfection at 2000 rpm for 90 min 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 (100 μM), 10 μl P7 primer mix (5 μM), 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 condition. 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

AATGATACGGCGACCACCGAGATCT

ACACTCTTTCCCTACACGACGCTCTTCCGATCT[s]TCTTGTGGAAAGG*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

CAAGCAGAAGACGGCATACGAGATNNNNNNNNGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTCTACTATTCTTTCCCCTGCA*C*T*G*T

Independently barcoded P7 primers was used for each condition.

NNNNNNNN – barcode region

Analysis of the shRNA screen

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A jupyter notebook, along with companion data files to reproduce the figures and analysis in this work, can be found at https://github.com/sankaranlab/shRNA_screen (Nandakumar and McFarland, 2019; copy archived at https://github.com/elifesciences-publications/shRNA_screen). To summarize, 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 at each time point since transfection (t) 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 that could accumulate over the course of the experiment. The resulting model, y ~ t + (0 + t|h), 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 (Global Lipids Genetics Consortium et al., 2013), and against all genes in the genome (Ensembl GRCh37p9). Coding variants and protein effects (i.e. missense or nonsense) were annotated based on the Variant Effect Predictor software (https://www.ensembl.org/vep). 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 was 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. For the comparison with eQTL-based methods of hit identification, we used the whole blood summary statistics from GTEx and intersected them with genomic regions in linkage disequilibrium r2 >0.8 with the 75 sentinel SNPs from the van der Harst et al. study used to identify the library of genes targeted in the screen (see Figure 1A). These regions were padded to a fixed 100 kb, as many of the regions were small. This yielded 139 genes that one could argue would be nominated on an eQTL-basis from the total pool of 8661 genes with a whole blood eQTL. We performed a Fisher’s exact test on the contingency table comparing hits from our method of nomination with the set of eQTLs and eQTL-nominated genes. There were 35 hits (of the 77 total in our screen) present among the whole eQTL set, but 0 hits found among the 139 genes nominated by using eQTLs from whole blood. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on 06/01/18.

RNA-Seq

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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 × 38 bp paired end reads.

RNA-seq differential expression analysis

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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 was 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

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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). The software’s default cutoff of Bayes factor of 10 or greater was used to call differential splice forms.

Analysis of hemoglobin levels for MDS patients with or without SF3A2 mutations

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Genotyped MDS patient hemoglobin level measurements were obtained from the laboratory of J. Maciejewski. 1000GENOMES phase three data were 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

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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 2 U/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

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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 four 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

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Bone marrow cells that were isolated from Sf3b1K700E +/- mice and littermate controls were lineage depleted using the 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 2000 rpm 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 hr. Following this, the cells were cultured for another 48 hr 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

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All flow cytometry data were 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 min 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’_TGCTGTTGACAGTGAGCGATCTCGCTTGGGCGAGAGTAAGTAGTGAAGCCACAGATGTACTTACTCTCGCCCAAGCGAGAGTGCCTACTGCCTCGGA_3’

Sf3a2 sh1 (mouse)

5’_TGCTGTTGACAGTGAGCGCGGAGGTGAAGAAGTTTGTGAATAGTGAAGCCACAGATGTATTCACAAACTTCTTCACCTCCATGCCTACTGCCTCGGA_3’

Sf3a2 sh2 (mouse)

5’_TGCTGTTGACAGTGAGCGACCACCGTTTCATGTCTGCTTATAGTGAAGCCACAGATGTATAAGCAGACATGAAACGGTGGCTGCCTACTGCCTCGGA_3’

Sf3a2 sh3 (mouse)

5’_TGCTGTTGACAGTGAGCGATCCTGCCTTGAGCCTATTAAATAGTGAAGCCACAGATGTATTTAATAGGCTCAAGGCAGGACTGCCTACTGCCTCGGA_3’

Sf3a2 sh4 (mouse)

5’_TGCTGTTGACAGTGAGCGACCACTGGAACAGAGAAACCAATAGTGAAGCCACAGATGTATTGGTTTCTCTGTTCCAGTGGGTGCCTACTGCCTCGGA_3’

Sf3a2 sh5 (mouse)

5’_TGCTGTTGACAGTGAGCGATGGAGGTGAAGAAGTTTGTGATAGTGAAGCCACAGATGTATCACAAACTTCTTCACCTCCACTGCCTACTGCCTCGGA_3’

sgRNA sequences

Request a detailed protocol

The following sgRNA sequences targeting a variant in the Duffy promoter were cloned into SpCas9 and KRAB-dcas9 constructs.

Duffy sgRNA 3: 5’-GGCCCGCAGACAGAAGGGCT-3’

Duffy sgRNA 5: 5’-GGGCCATCAGGTTCTGGGCA-3’

Control sgRNA: 5’-ATCGCGAGGACCCGTTCCGCC-3’

qPCR primers

Request a detailed protocol

TFR2 Fwd: 5'-ATCCTTCCCTCTTCCCTCCC-3'

TFR2 Rev: 5'-CCATCCAGCCACATGGTTCT-3

SF3A2 Fwd: 5'-CCTGAGAAGGTCAAGGTGGA-3'

SF3A2 Rev: 5'-CTCCGAGTCTCTCTGCTTGG-3'

Western blot antibodies

Request a detailed protocol

Anti-GAPDH (Santa Cruz Biotechnology, sc-32233); anti-TFR2 (Santa Cruz Biotechnology, sc- sc-32271); anti-SF3A2 (Santa Cruz Biotechnology, sc-390444).

Source data

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Important data associated with figures in the manuscript are included below. For a full set of datasets and resources used in the analyses, please see the companion GitHub repository (https://github.com/sankaranlab/shRNA_screen).

Data availability

1000 Genomes human variation dataset: The 1000 Genomes Project Consortium. (2015) Recombinant hotspots access at: ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/pilot_data/technical/reference/. Phase 1 data (for PLINK) accessed at: https://www.cog-genomics.org/plink/1.9/resources. Phase 3 data accessed at: http://www.internationalgenome.org/category/phase-3/. Pooled screen abundance data for shRNA targeting red blood cell trait GWAS-nominated genes during the course of in vitro differentiation of human CD34+ cells (SK Nandakumar, SK McFarland et al., 2019) is available on the project's companion GitHub repository: https://github.com/sankaranlab/shRNA_screen/tree/master/ref/shref.csv (copy archived at https://github.com/elifesciences-publications/shRNA_screen).

The following data sets were generated
The following previously published data sets were used
    1. Global Lipids Genetics Consortium
    (2013) Center for Statistical Genetics
    SNP sets identified by GWAS of LDL, HDL, and triglyceride traits.

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Decision letter

  1. Stephen Parker
    Reviewing Editor; University of Michigan, United States
  2. Mark I McCarthy
    Senior Editor; University of Oxford, United Kingdom

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Gene-centric functional dissection of human genetic variation uncovers regulators of hematopoiesis" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Stephen Parker as the Reviewing Editor, and the evaluation has been overseen by Mark McCarthy as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

Nandakumar et al. set out to study RBC GWAS signals in a model system of HSPCs differentiated to the erythroid lineage. They select ~400 genes across 75 loci and perform a shRNA screen quantifying shRNA abundance across differentiation time points. Their screen identifies 77 gene hits at 38 loci and they follow-up with some biological insight into two of these genes (TFR2 and SF3A2). Overall, this innovative approach provides a nice model for the field but we are unsure about a few important items that we elaborate on below.

Essential revisions:

1) The idea used to set up the screen and motivate gene selection by looking in the LD/recombination block needs to be further justified. Given most GWAS hits are non-coding, what is the any strong reason to assume the block must extend to the target gene, and how do we know most GWAS are not like the FTO locus that interacts with far-away genes? This is something that should be justified early on. As-is, this idea is only mentioned briefly in the Discussion.

2) As a corollary to point 1, a more conventional approach to connect GWAS to target genes is through eQTL analyses. Did the authors look at eQTL in whole blood (for which there are large data sets available) or other relevant cell types? How concordant are eQTL nominated genes with the genes revealed in the shRNA screen? The status of overlap and missingness would both be interesting to comment on here. Further, where eQTL data can nominate a target gene and the gene emerges from the shRNA screen, does the eQTL allelic direction of the RBC trait lead to reduced target gene expression (which would be consistent with the knock-down effects of shRNA treatment)? If the allelic direction of effect is different, how does one interpret this?

3) The screen quantified shRNA abundance as a function of erythroid differentiation and they identified targets that were either enriched or depleted late in the differentiation process. It is unclear what the significance of being enriched or depleted in the library means in terms of erythroid differentiation. Are there known genes that have biological functions which are expected to be enriched and depleted if they are depleted by shRNA?

4) Why do the authors use shRNA instead of CRISPR knockout or knockdown? There are numerous references (going back to early 2000s) documenting off-target effects of shRNA, so it is puzzling why this choice was made. For example, this recent preprint from George Daley's lab: "Short hairpin RNAs artifactually impair cell growth and suppress clustered microRNA expression." https://www.biorxiv.org/content/early/2018/12/11/372920 Given the "artifactual impair[ment] of cell growth" with shRNA, do the authors feel that their primary readout (growth) can be trusted given the shRNA perturbation?

5) Related to the concern mentioned in point 5, the authors should consider using CRISPRi (or CRISPR) to validate a subset of loci. Perhaps the two genes they've already focused on at the end. If such data are already (nearly) available (maybe in anticipation of such a reviewer comment), we encourage the authors to include it in this work. Although we list this in the essential revisions section and feel strongly about it, we realize this work may not be feasible within two months. If that's the case, we expect strong justification and text that directly addresses the point of shRNA vs CRISPR(i) screening approaches.

https://doi.org/10.7554/eLife.44080.040

Author response

Essential revisions:

1) The idea used to set up the screen and motivate gene selection by looking in the LD/recombination block needs to be further justified. Given most GWAS hits are non-coding, what is the any strong reason to assume the block must extend to the target gene, and how do we know most GWAS are not like the FTO locus that interacts with far-away genes? This is something that should be justified early on. As-is, this idea is only mentioned briefly in the Discussion.

Thank you for identifying this as an area for further clarification. While the method we use to nominate genes included in the shRNA screen may miss longer range interactions, when the screen was designed it seemed to represent a reasonable approach to capture a portion of the genetic targets for non-coding genetic variants and was similar to other approaches used to nominate genes that had been described in the literature. Since the screen was designed and executed, studies like Whalen and Pollard, 2019 have emerged which provide an enhanced sense of which topological tools at our disposal can offer the most comprehensive approach to target selection. While the use of strong LD partners by itself may not capture a majority of gene targets, by supplementing it with extension to the nearest recombination junctions and by adding wingspans on to the 5’ and 3’ end of candidate genes, we do actually enter into a regime where we would estimate we could resolve over half of likely targets. In Whalen and Pollard, 2019, the median eQTL distance is noted to be 49 kb, which falls well within the bulk of our probability density for the size of the SNP windows we are generating. Regardless, we agree that this issue could have been explored more deeply in the text, so we have made amendments to reflect that discussion.

Results:

“We endeavored to select candidate genes that could potentially underlie these 75 GWAS signals. […] However there still exists a nontrivial amount of valid targets within reach of proximity LD approaches, especially when the calculation of such windows are extended to reach the nearest recombination hotspot, suggesting that our approach would capture many candidate target genes.”

2) As a corollary to point 1, a more conventional approach to connect GWAS to target genes is through eQTL analyses. Did the authors look at eQTL in whole blood (for which there are large data sets available) or other relevant cell types? How concordant are eQTL nominated genes with the genes revealed in the shRNA screen? The status of overlap and missingness would both be interesting to comment on here. Further, where eQTL data can nominate a target gene and the gene emerges from the shRNA screen, does the eQTL allelic direction of the RBC trait lead to reduced target gene expression (which would be consistent with the knock-down effects of shRNA treatment)? If the allelic direction of effect is different, how does one interpret this?

To examine the eQTL-based approach and how it might relate to our results, we took the whole blood gene expression summary statistics from GTEx and intersected them with genomic regions in linkage disequilibrium r2 > 0.8 with the 75 sentinel SNPs from van der Harst et al. study used to identify the library of genes targeted in the screen (see Figure 1A). These regions were padded to a fixed 100 kb, as many of the regions are fairly small. This yielded 139 genes that one could argue would be nominated on an eQTL-basis from the total pool of 8661 examined. We performed a Fisher’s exact test on the contingency table comparing hits from our method of nomination with the set of eQTLs and eQTL-nominated genes. There were 35 hits (of the 77 total in our screen) present among the whole eQTL set, but 0 hits found among the 139 genes nominated by using eQTLs from whole blood. Thus, there was no appreciable connection between the eQTL-based method and our nomination method. We suggest that this observation also emphasizes how studies of variation in developing hematopoietic cells may not be accurately reflected by studies of circulating blood cells. We are looking at erythroblasts which are essentially not present in whole blood, so one would expect to miss cell type specific effects or eQTLs that act in early progenitor populations. Substantial additional work would be required to disentangle this further, but this is in keeping with our position that our nomination approach offers valuable information that is complementary to other typical means of nomination.

3) The screen quantified shRNA abundance as a function of erythroid differentiation and they identified targets that were either enriched or depleted late in the differentiation process. It is unclear what the significance of being enriched or depleted in the library means in terms of erythroid differentiation. Are there known genes that have biological functions which are expected to be enriched and depleted if they are depleted by shRNA?

We apologize if we did not do an adequate job elaborating on this in the text and connecting to the relevant panels (especially Figure 2C-E). We have made a number of changes which we hope address this point by more explicitly highlighting the outcomes of the included controls.

Results:

“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). […] The set of control shRNAs encompassed essential housekeeping genes as positive controls, negative controls in the form of luciferase and other genes not expressed in humans, and a well-established set of genes known to be important for erythropoiesis (erythroid controls) (Figure 1B).”

“The infected HSPCs were cultured using a three-phase semi-synchronous erythroid differentiation method where differentiation blockade reduces cell numbers either through cell death or through a failure of proliferation (Giani et al., 2016; Hu et al., 2013). 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).”

“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 2B). […] Hairpins targeting genes with known biological roles in erythropoiesis, such as GATA1 and RPS19 (Khajuria et al., 2018; Ludwig et al., 2014), showed markedly decreased abundance across the time course.”

4) Why do the authors use shRNA instead of CRISPR knockout or knockdown? There are numerous references (going back to early 2000s) documenting off-target effects of shRNA, so it is puzzling why this choice was made. For example, this recent preprint from George Daley's lab: "Short hairpin RNAs artifactually impair cell growth and suppress clustered microRNA expression." https://www.biorxiv.org/content/early/2018/12/11/372920 Given the "artifactual impair[ment] of cell growth" with shRNA, do the authors feel that their primary readout (growth) can be trusted given the shRNA perturbation?

We understand the reviewers’ concern that shRNAs can have off-target effects and can impair cell growth. CRISPR based screening approaches can potentially overcome these issues. We chose to perform our screen in primary human CD34+ hematopoietic stem and progenitor cells (HSPCs), instead of using cell lines, to identify the bonafide regulators of erythropoiesis that could underlie common genetic variation. We have tested both CRISPR and shRNA-based approaches in primary HSPCs and our data, which we now added to Figure 1—figure supplement 2, suggest that CRISPR based pooled screens are not feasible to perform in primary HSPCs, given low levels of infection that are achieved. On the other hand, in our experience, results from shRNA experiments can be independently validated using CRISPR based approaches (Gianni et al., 2016). These results have motivated us to perform an shRNA screen in primary HSPCs and we can increase our confidence in the growth readout by employing 5-7 shRNAs per gene and compared them against a pool of negative control shRNAs.

We observe that all in one CRISPR/ CRISPRi lentiviral constructs are produced at lower viral titer (~5e5 infectious units /ml unconcentrated) compared to shRNA constructs (~ 9e6 infectious units /ml unconcentrated). This is likely due, at least in part, to reduced packaging ability, owing to the large size of the Cas9 cDNA (~4.2kb) compared to shRNAs (21bp) (Figure 1—figure supplement 2A). This low viral titer becomes a bottleneck when performing CRISPR screens in primary CD34+ HSPCs that require high multiplicities of infection (MOI) for successful gene transfer compared to rapidly dividing cell lines that require a low MOI.

In our initial studies, we observed that shRNA constructs were able to achieve good infectivity in CD34+ HSPCs at low MOI (40-60% infectivity at MOI ~20) (Figure 1—figure supplement 2B). On the other hand, CRISPR/CRISPRi constructs require very high MOI (20- 30% infectivity at MOI 150) to achieve reasonable infectivity. We also observe that CRISPRi based approaches gave uniform knockdown compared to cutting with CRIPSR/Cas9 when we targeted a surface antigen, Duffy/DARC (Figure 1—figure supplement 2C). Based on our calculations we would require 3000 ml of virus to perform just one replicate (10 e6 CD34+ cells) of a CRISPR/CRISPRi based screen with sufficient library representation, which would certainly stretch and likely exceed what we are capable of achieving reasonably for such a functional screen.

A recent study published a few months ago has overcome this bottleneck by electroporation of Cas9 as a protein followed by infection with a pooled sgRNA lentiviral library, which can be produced at higher viral titers (Ting et al., 2019). This could be a promising means to use CRISPR in future screening approaches, although this approach is not available in the CRISPRi format.

Results:

“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). […] 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.”

5) Related to the concern mentioned in point 5, the authors should consider using CRISPRi (or CRISPR) to validate a subset of loci. Perhaps the two genes they've already focused on at the end. If such data are already (nearly) available (maybe in anticipation of such a reviewer comment), we encourage the authors to include it in this work. Although we list this in the essential revisions section and feel strongly about it, we realize this work may not be feasible within two months. If that's the case, we expect strong justification and text that directly addresses the point of shRNA vs CRISPR(i) screening approaches.

We appreciate this question to validate the shRNA screen results using orthogonal approaches such as CRISPR or CRISPRi. To this end, we have attempted to knockout one of the hits from the screen (TFR2) in HSPCs using Cas9 ribonucleoprotein (RNP) nucleofection. We selected 3 guides targeting TFR2 with high on-target scores based on the metrics reported by Doench et al., Nature Biotechnology, 2016. While we did observe reasonable editing rates, these were not stable over the time course of the experiment for currently unclear reasons. Since the edits are not stable, the outcomes of these experiments are hard to decipher without extensive additional characterization.

https://doi.org/10.7554/eLife.44080.041

Article and author information

Author details

  1. Satish K Nandakumar

    1. Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, United States
    2. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, United States
    3. Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Conceptualization, Formal analysis, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing
    Contributed equally with
    Sean K McFarland
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7853-426X
  2. Sean K McFarland

    1. Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, United States
    2. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, United States
    3. Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Conceptualization, Formal analysis, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing
    Contributed equally with
    Satish K Nandakumar
    Competing interests
    No competing interests declared
  3. Laura M Mateyka

    1. Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, United States
    2. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, United States
    3. Broad Institute of MIT and Harvard, Cambridge, United States
    4. Biochemistry Center (BZH), Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
    Contribution
    Validation, Investigation, Writing—review and editing
    Contributed equally with
    Caleb A Lareau and Jacob C Ulirsch
    Competing interests
    No competing interests declared
  4. Caleb A Lareau

    1. Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, United States
    2. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, United States
    3. Broad Institute of MIT and Harvard, Cambridge, United States
    4. Program in Biological and Medical Sciences, Harvard Medical School, Boston, United States
    Contribution
    Formal analysis, Writing—review and editing
    Contributed equally with
    Laura M Mateyka and Jacob C Ulirsch
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4179-4807
  5. Jacob C Ulirsch

    1. Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, United States
    2. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, United States
    3. Broad Institute of MIT and Harvard, Cambridge, United States
    4. Program in Biological and Medical Sciences, Harvard Medical School, Boston, United States
    Contribution
    Formal analysis, Writing—review and editing
    Contributed equally with
    Laura M Mateyka and Caleb A Lareau
    Competing interests
    No competing interests declared
  6. Leif S Ludwig

    1. Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, United States
    2. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, United States
    3. Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  7. Gaurav Agarwal

    1. Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, United States
    2. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, United States
    3. Broad Institute of MIT and Harvard, Cambridge, United States
    4. University of Oxford, Oxford, United Kingdom
    5. Harvard Stem Cell Institute, Cambridge, United States
    Contribution
    Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  8. Jesse M Engreitz

    1. Broad Institute of MIT and Harvard, Cambridge, United States
    2. Harvard Society of Fellows, Harvard University, Cambridge, United States
    Contribution
    Formal analysis, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5754-1719
  9. Bartlomiej Przychodzen

    Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Formal analysis, Writing—review and editing
    Competing interests
    No competing interests declared
  10. Marie McConkey

    Division of Hematology, Brigham and Women’s Hospital, Boston, United States
    Contribution
    Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  11. Glenn S Cowley

    Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Formal analysis, Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  12. John G Doench

    Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Formal analysis, Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3707-9889
  13. Jaroslaw P Maciejewski

    Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, United States
    Contribution
    Formal analysis, Writing—review and editing
    Competing interests
    No competing interests declared
  14. Benjamin L Ebert

    1. Broad Institute of MIT and Harvard, Cambridge, United States
    2. Division of Hematology, Brigham and Women’s Hospital, Boston, United States
    3. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States
    4. Howard Hughes Medical Institute, Chevy Chase, United States
    Contribution
    Formal analysis, Writing—review and editing
    Competing interests
    No competing interests declared
  15. David E Root

    Broad Institute of MIT and Harvard, Cambridge, United States
    Contribution
    Conceptualization, Formal analysis, Writing—review and editing
    Competing interests
    No competing interests declared
  16. Vijay G Sankaran

    1. Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, United States
    2. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, United States
    3. Broad Institute of MIT and Harvard, Cambridge, United States
    4. Harvard Stem Cell Institute, Cambridge, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    sankaran@broadinstitute.org
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0044-443X

Funding

National Institutes of Health (R33HL120791)

  • Vijay G Sankaran

New York Stem Cell Foundation

  • Vijay G Sankaran

National Institutes of Health (R01DK103794)

  • Vijay G Sankaran

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

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 VGS). VGS is a New York Stem Cell Foundation-Robertson Investigator.

Senior Editor

  1. Mark I McCarthy, University of Oxford, United Kingdom

Reviewing Editor

  1. Stephen Parker, University of Michigan, United States

Publication history

  1. Received: December 2, 2018
  2. Accepted: May 8, 2019
  3. Accepted Manuscript published: May 9, 2019 (version 1)
  4. Version of Record published: May 24, 2019 (version 2)

Copyright

© 2019, Nandakumar et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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