Transcriptome analyses of human and murine reveal significant stage and species-specific differences across stages of terminal erythroid differentiation.
These transcriptomes provide a significant resource for understanding mechanisms of normal and perturbed erythropoiesis.
We recently developed fluorescence-activated cell sorting (FACS)-based methods to purify morphologically and functionally discrete populations of cells, each representing specific stages of terminal erythroid differentiation. We used these techniques to obtain pure populations of both human and murine erythroblasts at distinct developmental stages. RNA was prepared from these cells and subjected to RNA sequencing analyses, creating unbiased, stage-specific transcriptomes. Tight clustering of transcriptomes from differing stages, even between biologically different replicates, validated the utility of the FACS-based assays. Bioinformatic analyses revealed that there were marked differences between differentiation stages, with both shared and dissimilar gene expression profiles defining each stage within transcriptional space. There were vast temporal changes in gene expression across the differentiation stages, with each stage exhibiting unique transcriptomes. Clustering and network analyses revealed that varying stage-specific patterns of expression observed across differentiation were enriched for genes of differing function. Numerous differences were present between human and murine transcriptomes, with significant variation in the global patterns of gene expression. These data provide a significant resource for studies of normal and perturbed erythropoiesis, allowing a deeper understanding of mechanisms of erythroid development in various inherited and acquired erythroid disorders.
Mammalian erythropoiesis is an excellent example of the complex changes in temporal, developmental, and differentiation stage-specific gene expression exhibited by a single cell type.1,2 In the mammalian embryo and fetus, erythroid cells have differing developmental origins, with the primitive erythroid cell lineage developing from yolk sac–derived erythroid progenitors, and the definitive cell lineage maturing from 2 different developmentally regulated stem and progenitor cell populations.3-6 These cells have different programs of regulation, with variation in spatial, temporal, and site-specific differentiation.
In the adult, mature erythrocytes are the terminally differentiated final cellular product derived from hematopoietic stem and progenitor cells (HSPC). HSPCs undergo a series of lineage choice fate decisions, with increasingly restricted potential, ultimately committing to the erythroid lineage and beginning erythropoiesis. Traditionally, erythropoiesis has been divided into 3 stages: early erythropoiesis, terminal erythroid differentiation, and reticulocyte maturation.2 Early erythropoiesis involves commitment of multi-lineage progenitors into erythroid progenitor cells, with proliferation and differentiation into erythroid burst-forming unit cells, followed by erythroid colony-forming unit cells, then differentiation into proerythroblasts. Terminal erythroid differentiation begins with proerythroblasts differentiating into basophilic, then polychromatic, then orthochromatic erythroblasts that enucleate to become reticulocytes. Numerous changes occur during terminal erythroid differentiation. Erythroblasts decrease in size, synthesize increasing amounts of hemoglobin, undergo membrane reorganization and chromatin condensation, and then enucleate.7,8 In the final stage of erythropoiesis, reticulocytes mature into discoid erythrocytes, losing intracellular organelles, decreasing cell volume and surface area, and reorganizing the erythrocyte membrane.
Rapid advances in genomic technologies, particularly those coupled to high-throughput sequencing technologies, have revolutionized our understanding of gene expression, gene regulation, and mechanisms of human disease.9 RNA sequencing (RNA-seq) allows unbiased detection and quantification of transcriptomes using high-throughput sequencing.10,11 Beyond providing unbiased detection of transcripts, it provides information on transcript composition and abundance, including detection of novel transcripts, isoforms, alternative splice sites, allele-specific expression, and rare transcripts.11-13 RNA-seq has a low background signal and a large dynamic range, with high levels of reproducibility for both technical and biological replicates. The ability to determine detailed cellular transcriptomes has broad implications for interpreting the functional elements of the genome, revealing the molecular constituents of cells and tissues, and for understanding development and disease.
We have recently developed a fluorescence-activated cell sorting (FACS)-based method to obtain pure populations of human and murine erythroblasts at differing stages of terminal erythroid differentiation.14-16 RNA was prepared from these cells and subjected to RNA-seq analyses, creating unbiased differentiation stage–specific transcriptomes. Tight clustering of transcriptomes from differing stages validated the utility of the FACS-based isolation of erythroblasts at distinct stages of terminal differentiation. Marked differences were present between differentiation stages. Although there were many similarities, numerous differences were present between human and murine transcriptomes, with significant variation in the global patterns of gene expression. These data provide a significant resource for studies of normal and perturbed erythropoiesis, allowing a deeper understanding of mechanisms of erythroid development in various inherited and acquired erythroid disorders.
Materials and methods
Isolation of human and murine erythroblasts
CD34+ HSPCs were purified from cord blood by positive selection to a purity of 95% to 98% as described.15 A 3-phase CD34+ cell culture system was used to produce primary human erythroid cells at different stages of terminal differentiation. To obtain discrete populations of erythroid cells, a combination of cell surface markers for glycophorin A, band 3, and α4-integrin was used for FACS of cultured cells. This combination enables isolation of highly purified populations of erythroblasts at each distinct stage of terminal erythroid differentiation.15 Murine erythroblasts at distinct stages of terminal erythroid differentiation were sorted from bone marrow using TER119 antibody (anti-glycophorin A), CD44 antibody, and forward scatter (reflecting cell size) as markers.14,16
RNA isolation and preparation
RNA was prepared from primary human and murine erythroid cells for RNA-seq analyses as described.17 Quantitative real-time polymerase chain reaction (PCR) was performed to confirm expression levels of RNA transcripts (supplemental Table 1, available on the Blood Web site). Real-time PCR data were normalized as described.18
Library preparation and sequencing
RNA samples were treated with RNase-free DNase I (Takara, Otsu, Japan), quantified using a NanoDrop1000 (Thermo-Fisher, Waltham, MA), and assessed with an Agilent2100 Bioanalyzer (Agilent, Santa Clara, CA). The RNA integrity number of each sample was >9. DNA libraries were prepared according to the manufacturer’s instructions (Illumina, San Diego, CA). Each library was sequenced on the Illumina HiSeq2000 platform using a 50-bp single-end, nonstrand-specific sequencing strategy.
Detailed description of data analyses are provided in the supplemental Data.
Cell isolation and RNA-seq
Highly purified human and murine erythroid cells corresponding to distinct stages of terminal erythroid differentiation were isolated using a recently developed FACS-based method as described. RNA was isolated from these cells and subjected to RNA-seq analyses to identify unbiased gene expression profiles across the entire transcriptome during terminal erythroid differentiation. Deep sequencing was performed on polyA+ messenger RNA (mRNA) from 3 biological replicates of each stage. Data were analyzed and subjected to quality control analyses. We analyzed transcriptomes using TopHat and Cufflinks packages generating high-confidence transcriptomes for each differentiation stage.13 Differences in levels of expression were analyzed by edgeR.19 Quantitative real-time PCR was performed to validate expression levels of representative mRNA transcripts detected by RNA-seq (supplemental Figure 1). Only annotated genes were analyzed.
Initially, we analyzed how many annotated genes were expressed at each differentiation stage. On average, we detected expression between 4804 human RefSeq genes in orthochromatic erythroblasts and 9606 RefSeq genes in proerythroblasts (supplemental Table 2) and between 6584 murine RefSeq genes in orthochromatic erythroblasts and 8838 RefSeq genes in proerythroblasts. Thus approximately 20% to 40% of known human genes and 28% to 38% of known murine genes were expressed during terminal erythroid differentiation.
Transcriptome profiles support FACS-based sorting methodology
Principal component analysis) was performed on expressed genes in human erythroblasts (>10 count per million in 3 or more samples). Samples from individual stages of terminal erythroid differentiation clustered closely together (Figure 1A). Multidimensional scaling using edgeR, which places objects in dimensional space preserving the between-object distances, also revealed tight clustering of stage-specific biologic replicates (supplemental Figure 2). This tight clustering indicates that samples from each stage are very closely related. It also indicates that each stage of terminal erythroid differentiation is likely functionally distinct.
Pairwise comparisons between adjacent stages of differentiation (Figure 1B; supplemental Figure 3) revealed very high Pearson correlation coefficients between replicates, range 0.94-0.99, indicating high reproducibility between stage-specific replicates (proerythroblast 0.95-0.97; early basophilic erythroblast 0.95-0.97; late basophilic erythroblast 0.95-0.97; polychromatic erythroblast 0.97-0.98; orthochromatic erythroblast 0.94-0.99). These comparisons also revealed dramatic changes in gene expression profiles across differentiation stages. The tight clustering of samples from individual stages of terminal erythroid differentiation indicate that transcriptome analyses support the FACS-based methodology used to define differentiation stages, even between biologically different replicates. It also indicates that differentiation stages are distinct, with both shared and dissimilar gene expression profiles defining each stage within transcriptional space. There are vast changes in gene expression across the differentiation stages, with each stage exhibiting unique transcriptomes.
Temporal patterns of gene expression during human terminal erythroid differentiation
To further analyze transcriptome profiles across the stages of human terminal erythroid differentiation, we analyzed RNA-seq data using edgeR, a Bioconductor software package for examining differential expression of replicated count data.20-22 Pairwise comparisons of all adjacent stages of differentiation were performed. Large numbers of genes were differentially expressed at different stages of erythroid differentiation; of the 9931 genes expressed in 3 or more samples, more than a quarter (2702) were differentially expressed (false discovery rate [FDR] <0.01, fold change >4; supplemental Table 3). The greatest changes in differential gene expression were seen between the late basophilic to polychromatic and the polychromatic to orthochromatic stages (Figures 2A and 3A), demonstrating that the most dramatic changes occur at the late stages of terminal differentiation.
Venn diagram analyses revealed that differentially expressed genes found in 1 comparison were sometimes, but not usually, found in other comparisons (supplemental Figure 4), demonstrating there are stage-specific changes in differential gene expression.
Differentiation stages are transcriptionally enriched for genes of differing function
We analyzed the expression patterns of all differentially expressed, coregulated genes from the adjacent stage pairwise comparisons using k-means clustering. The differentially expressed genes clustered into 6 major groups, demonstrated in heat map and graphical format, based on patterns of expression at the differing time points (Figure 4). Two patterns revealed low- to mid-range levels of expression in proerythroblasts, increasing during differentiation (groups 1 and 2), whereas 2 patterns demonstrated high levels of expression in proerythroblasts, decreasing during differentiation (groups 3 and 4).The final 2 patterns were V-shaped mirror images, with either a peak or a nadir at the polychromatic erythroblast stage (groups 5 and 6).
We performed gene ontology (GO) analysis of differentially expressed genes within these 6 clusters and identified associated enriched GO terms to gain insights into the biological processes regulated during terminal erythroid differentiation (Figure 4).23 In groups 1 and 2 with low- to mid-range levels of expression in proerythroblasts that increased during differentiation, GO terms significantly enriched (FDR <0.01) for differentially expressed genes related to cellular catabolism and cell death were identified. In groups 3 and 4 with high levels of expression in proerythroblasts that decreased during differentiation, GO terms significantly enriched for differentially expressed genes related to protein synthesis including translation and ribosome biogenesis, and DNA metabolism including replication, repair, and cell cycle were identified. Finally, for groups with V-shaped patterns, GO terms significantly enriched for differentially expressed genes were related to cell cycle and cell division (peak at polychromatic erythroblast stage) or protein folding and noncoding RNA metabolism (nadir at polychromatic erythroblast stage). Examples of genes with different patterns of expression across the stages of terminal erythroid differentiation—FOX03, STOM, PKLR, and STAT5A—are shown in Figure 5.
Ingenuity Pathway Analysis of the differentially expressed genes within the 6 clusters was performed to gain additional insights into the biological processes regulated during terminal erythroid differentiation. The top functional networks, defined as scores >20, yielded results similar to the GO analyses (supplemental Table 4). For example, in groups 1 and 2, with low- to mid-range levels of expression in proerythroblasts increasing during differentiation, Ingenuity Pathway Analysis identified networks associated with RNA and DNA metabolism, cell cycle, and protein synthesis. Interestingly, for 2 of the top networks, the multifunctional serine/threonine protein kinase AKT was at the major organizing node (not shown). Ingenuity Pathway Analysis analyses of the term “molecular and cellular functions” yielded similar results (supplemental Table 5).
Marked variation in transcriptome composition during human terminal erythroid differentiation
This analysis indicated that, at the transcriptome level, there are vast differences between stages of terminal erythroid differentiation. To better compare and contrast these differences, we compared cells from different stages of erythroid differentiation with HSPCs. HSPCs are at the apex of the hierarchy of hematopoietic cell development, commitment, and differentiation. They not only give rise to all hematopoietic cell types, they also have the ability to self-renew. We used Pearson correlation coefficient analysis and multidimensional scaling analysis with edgeR to compare HSPCs with proerythroblasts and orthochromatic erythroblasts. Multidimensional scaling allows visualization of the levels of similarity between datasets by placing each data point in dimensional space while preserving the space between object distances with each object assigned dimensional coordinates. As expected, there were significant differences in expression between HSPCs and proerythroblasts (Pearson correlation coefficient = 0.84; supplemental Figure 5). Remarkably, the degree of difference between HSPCs and proerythroblasts was nearly identical to the degree of difference between proerythroblasts and orthochromatic erythroblasts (Pearson correlation coefficient = 0.88; Figure 6).
Highly expressed transcripts encode hemoglobin-related proteins
We assembled lists of the highest expressed genes (by reads per kilobase of transcript per million) at each stage of erythroid differentiation (Table 1). These genes were primarily associated with hemoglobin synthesis, structure, and function. These included the globin genes, δ-aminolevulinate synthase 2 (ALAS2), α hemoglobin stabilizing protein (AHSP) (the hemoglobin chaperone), and proteins involved in iron metabolism including the transferrin receptor and light and heavy ferritin chains. In addition, many ribosomal genes were expressed at very high levels, particularly in proerythroblasts and early basophilic erythroblasts.
|Rank||Proerythroblast||Early basophilic erythroblast||Late basophilic erythroblast||Polychromatic erythroblast||Orthochromatic erythroblast|
|Rank||Proerythroblast||Early basophilic erythroblast||Late basophilic erythroblast||Polychromatic erythroblast||Orthochromatic erythroblast|
Transcription factor gene expression throughout human erythroid differentiation
Transcription factors are proteins that detect and bind to DNA regulatory sequences and participate in the assembly of multiprotein complexes that regulate gene expression. Recent studies have begun to dissect the transcriptional networks that regulate hematopoietic cell fate.24-26 We analyzed transcription factor gene expression across human terminal erythroid differentiation. Similar to global patterns of gene expression, transcription factors demonstrated varying patterns of differential expression throughout differentiation (supplemental Figure 6A; supplemental Table 6). Analysis of transcription factors by absolute expression revealed several critical erythroid transcription factors. The top 5 transcription factors were KLF1, NFE2, GFI1B, YBX1, and GATA1 (supplemental Figure 6B).
Transcriptomes in murine terminal erythroid differentiation
We used similar FACS-based strategies to obtain purified populations of murine proerythroblasts, basophilic, polychromatic, and orthochromatic erythroblasts and subjected them to RNA-seq. Analysis of murine transcriptome profiles across the stages of terminal erythroid differentiation using edgeR revealed that large numbers of genes were differentially expressed at different stages of erythroid differentiation; in 9096 of 23 283 genes expressed in 3 or more samples, 2288 were differentially expressed (FDR <0.01, fold change >4).
Near global decrease in gene expression during murine terminal erythroid differentiation
We analyzed the expression patterns of all differentially expressed, coregulated genes from the adjacent stage pairwise comparisons using k-means clustering. The differentially expressed genes clustered into 3 major groups, demonstrated in heat map and graphical format, based on patterns of expression at the differing time points (supplemental Figure 7). There was 1 predominant pattern of gene expression with high levels of gene expression in proerythroblasts that decreased steadily during differentiation (group 1; supplemental Figure 7). Group 2 had an expression pattern similar to group 1. We performed GO analysis of differentially expressed genes within these 3 clusters and identified associated enriched GO terms to gain insight into the biological processes regulated during murine terminal erythroid differentiation (supplemental Figure 7).23 In groups 1 and 2, both with high levels of expression in proerythroblasts decreasing during differentiation, GO terms were significantly enriched (FDR = 0.01) for differentially expressed genes related to protein synthesis including translation and ribosome biogenesis, and DNA metabolism including replication, repair, and cell cycle. This is similar to the terms enriched in the clusters of human genes with decreasing expression during terminal erythroid differentiation.
We assembled lists of the highest expressed genes (by reads per kilobase of transcript per million) at each stage of erythroid differentiation (supplemental Table 7). These genes were primarily associated with hemoglobin synthesis and proteins involved in iron metabolism.
Comparison of human and murine transcriptomes
Additional analyses of the differences between human and murine terminal erythroid differentiation were performed to begin to understand the extent of similarities and differences as well as their potential functional consequences. Because initial human and mouse transcriptome analyses were done independently, an integrated dataset of expression values for orthologous genes between human and mouse for identical stages of terminal differentiation was created. Principal component analyses revealed general similarity in the global trend in patterns of gene expression between species during erythroid differentiation (supplemental Figure 8).
Hierarchical clustering of the combined species data set of orthologous genes from each stage revealed significant differences in gene expression between human and mouse (Figure 2C). These data demonstrating human/murine transcriptome differences were further analyzed to search for potential functional differences between species. Eleven clusters of orthologous genes (>100 genes/cluster) with different patterns of expression in human compared with mouse during terminal erythroid differentiation were identified and Database for Annotation, Visualization and Integrated Discovery (DAVID) analyses performed (supplemental Table 8). The goal of DAVID analyses is to identify enriched biological themes and discover functional-related gene groups. DAVID analyses identified numerous categories of varying cellular processes (supplemental Table 8). Although these analyses cannot precisely address how differing transcriptomes contribute to species-specific differences between human and mouse, the data indicate that numerous, complex, and differing patterns of gene expression likely regulate the complicated process of terminal erythroid differentiation in both species.
In addition to the global differences in patterns of gene expression during terminal erythroid differentiation between human and mouse noted previously, numerous other dissimilarities were noted. Whereas there were several patterns of transcription factor gene expression in human, patterns of transcription factor gene expression in mouse differed significantly, more similarly paralleling the gradual decrease in gene expression during murine terminal erythroid differentiation (supplemental Figure 9). Interestingly, growth differentiation factor 15 (GDF15), one of the top 25 expressed genes in human erythroid cells (Table 1), was not expressed in murine erythroid cells.
Another example of significant difference between human and murine terminal erythroid differentiation is in genes encoding proteins of the mitogen-activated protein kinase (MAPK) pathway. Of genes in this pathway, 113 of 268 genes were expressed in human and 95 of 269 genes were expressed in mouse. About half of the genes in human were downregulated during terminal erythroid differentiation (56 of 113), whereas three-quarters of genes in mouse were downregulated (72 of 95). In human, 30 of 113 expressed genes were upregulated during terminal erythroid differentiation, whereas only 3 genes were upregulated in mouse. Differentially expressed genes encoded proteins located at positions throughout the MAPK pathway (Figure 7A). Again, this pattern mirrors the global decrease in gene expression observed in murine terminal erythroid differentiation.
In the ubiquitin-mediated proteolysis pathway, genes associated with E1 ubiquitin–activating enzyme and E2 ubiquitin–conjugating enzyme were, for the most part, similarly expressed between human and mouse (not shown). However, many genes associated with E3 ubiquitin ligase and related proteins were upregulated in human but downregulated in mouse (Figure 7B). This is another example of downregulation of a large segment of a biologically relevant pathway of genes in mouse.
The stage-specific complexity of terminal erythroid differentiation has been studied for many years. Variation in many phenotypic features, such as metabolic properties, decrease in cell size, increases in hemoglobinization, alterations in membrane characteristics, epigenetic and nuclear changes with chromatin condensation, and, ultimately, enucleation, have led to the conclusion that terminal erythroid differentiation is a unique process, with each cell division simultaneously coupled with differentiation.2,7,8,14 In contrast to most cell types, in which each cell division generates 2 daughter cells that are nearly identical to the mother cell, during terminal erythroid differentiation, the 2 daughter cells are structurally and functionally different than the mother cell from which they are derived. This conclusion has been supported on a limited basis by gene expression analyses of varying populations of murine and human erythroid cells.8,27-35 Our transcriptome data strengthen these observations regarding the stage-specific complexity of erythroid differentiation.
The transcriptome data presented here also strongly support the recently described FACS-based strategy for identification of terminal differentiation stage-specific erythroid cells. Experiments were done in triplicate with different biologic replicates for each differentiation-specific stage to minimize individual-specific changes in expression of the ∼22 500 genes analyzed. This is a powerful technique that allows purification of pure populations of erythroid cells at specific stages of terminal erythroid differentiation for detailed studies of erythroid differentiation from primary human HSPCs.
Previous global gene expression studies of human and murine erythroid development and differentiation have used varying sources of erythroid cells representing primitive or definitive erythropoiesis.24,28,34,36-41 RNA was extracted from primary erythroid cells isolated without culture, or, after in vitro culture of nucleated cells from differing sources, eg, CD34+ HSPC or peripheral blood buffy coat. In most studies, cells were not purified by cell surface or other markers, but were selected at differing time points in vivo or in culture. In 1 study of global erythroid gene expression study, peripheral blood buffy coats were cultured in vitro then sorted by CD36, CD71, and CD235a expression and cell size to yield populations corresponding to erythroid colony-forming unit, proerythroblasts and intermediate and late erythroblasts. Transcriptomes constructed using stage-specific sorting strategies of primary, noncultured cells would be expected to yield the highest quality of transcriptome data. Because of limitations of human material, we used a 3-phase culture system starting with umbilical cord CD34+ cells to obtain erythroid cells. Depending on the source and age of the source of cells for analysis or culture, patterns of gene expression will represent fetal, neonatal, or adult programs of gene expression.40
These data sets can be used in a number of ways to interpret the transcriptional architecture of erythropoiesis. One is a better understanding of the basic biology of terminal erythroid differentiation at steady state, during stress erythropoiesis, and after various perturbations. For instance, these data can be interrogated to generate transcriptional circuits, as recently shown in hematopoiesis.24,25,42,43 In contrast to array-based studies, these RNA-seq–based studies allow creation and analysis of patterns of differentiation-stage, gene-specific isoform composition generated by RNA alternate splicing, cleavage, and polyadenylation.44-47 They also allow identification of long noncoding RNAs for use in functional analyses.48-50
Another important use for these data sets is to develop a better mechanistic understanding of disordered erythropoiesis, with the ability to better understand stage-specific defects. This includes disorders such as the thalassemia syndromes, bone marrow failure syndromes and aplastic anemia51-56 as well as acquired disorders such as the myelodysplasia syndromes,57-59 particularly subtypes with disordered terminal erythroid differentiation. Numerous abnormalities have been identified in these disorders, including perturbed apoptosis, cytokine signaling, and regulation of cellular growth.57,60-63 Comparative analyses between wild-type and variant cells may provide insight into disease pathobiology, allowing understanding of mechanisms of abnormal erythropoiesis over time in specific diseases, and may provide insights into identification of potential therapeutic targets.
These data also provide transcriptional context when interpreting data sets of genetic variants identified by genomewide sequencing in patients with hematologic disease from unknown causes (eg, when interpreting novel variants and analyzing gene expression in the process of variant analysis and interpretation).9
There is increasing understanding that there are fundamental differences between human and murine erythropoiesis. For instance, variation in glucose utilization, vitamin C metabolism, regulation of ion content and cell size, membrane protein composition and properties, and mechanisms of stress erythropoiesis are known differences between human and murine erythroid cells.30,64-70 Other differences include patterns of gene regulation (eg, the well-known dissimilarities in globin gene regulation) and differences in transcript isoform composition generated by alternate splicing, such as the differences in exon composition of the ALAS2 complementary DNA isoforms between mouse and man.71,72 One striking observation from our analyses was, in contrast to human, there was a near-global decrease in gene expression during murine terminal erythroid differentiation. This strongly suggests there are fundamental species-specific differences between human and murine erythropoiesis. The ability to compare and contrast gene expression profiles in human and murine terminal erythroid differentiation will provide novel insights into these differences between human and murine erythropoiesis while expanding our understanding of both.
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The online version of this article contains a data supplement.
The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.
This work was supported in part by grants from the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases (DK32094, DK26263, HL65448, and DK62039) and an American Society of Hematology Bridge Grant (X.A.).
Contribution: X.A. designed experiments, analyzed data, and wrote the manuscript; V.P.S. analyzed data and wrote the manuscript; J.L. performed experiments and analyzed data; J.L., J.P., and F.X. performed experiments; K.W. analyzed data; M.N. designed experiments and wrote the manuscript; and P.G.G. analyzed data and wrote the manuscript.
Conflict-of-interest disclosure: The authors declare no competing financial interests.
Correspondence: Patrick G. Gallagher, Departments of Pediatrics, Pathology, and Genetics, Yale University School of Medicine, 333 Cedar St, PO Box 208064, New Haven, CT 06520-8064; e-mail: email@example.com.