An erythroid-specific ATP2B4 enhancer mediates red blood cell hydration and malaria susceptibility
Samuel Lessard ... Daniel E. Bauer, Guillaume Lettre
Samuel Lessard ... Daniel E. Bauer, Guillaume Lettre
Published August 1, 2017
Citation Information: J Clin Invest. 2017;127(8):3065-3074. doi:10.1172/JCI94378.
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Categories: Research Article Genetics Hematology

An erythroid-specific ATP2B4 enhancer mediates red blood cell hydration and malaria susceptibility

Abstract

The lack of mechanistic explanations for many genotype-phenotype associations identified by GWAS precludes thorough assessment of their impact on human health. Here, we conducted an expression quantitative trait locus (eQTL) mapping analysis in erythroblasts and found erythroid-specific eQTLs for ATP2B4, the main calcium ATPase of red blood cells (rbc). The same SNPs were previously associated with mean corpuscular hemoglobin concentration (MCHC) and susceptibility to severe malaria infection. We showed that Atp2b4–/– mice demonstrate increased MCHC, confirming ATP2B4 as the causal gene at this GWAS locus. Using CRISPR-Cas9, we fine mapped the genetic signal to an erythroid-specific enhancer of ATP2B4. Erythroid cells with a deletion of the ATP2B4 enhancer had abnormally high intracellular calcium levels. These results illustrate the power of combined transcriptomic, epigenomic, and genome-editing approaches in characterizing noncoding regulatory elements in phenotype-relevant cells. Our study supports ATP2B4 as a potential target for modulating rbc hydration in erythroid disorders and malaria infection.

Authors

Samuel Lessard, Emily Stern Gatof, Mélissa Beaudoin, Patrick G. Schupp, Falak Sher, Adnan Ali, Sukhpal Prehar, Ryo Kurita, Yukio Nakamura, Esther Baena, Jonathan Ledoux, Delvac Oceandy, Daniel E. Bauer, Guillaume Lettre

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eQTL mapping in erythroblasts. (A) To identify eQTLs in erythroblasts (n...

Figure 1

eQTL mapping in erythroblasts.

(A) To identify eQTLs in erythroblasts (n = 24), we first focused on genes that show AI in at least 1 sample (n = 479 AI genes). Then we tested to determine whether SNPs located within 100 kb of these AI genes were associated with their expression level (left panel) and whether their genotypes were consistent with the expected AI ratio of reference allele/alternate allele (right panel). In this example, we highlight the candidate eQTL variant rs7287869 that is associated with the expression of the AI gene FAM118A. (B) Quantile-quantile plot of eQTL P values for variants located within 100 kb of 479 AI genes in human erythroblasts (black). Given that this analysis is limited to AI genes, we expected to observe a strong inflation of the eQTL test statistics (λGC = 1.25). In comparison, the inflation is reduced (λGC = 1.14) when analyzing variants located near 479 randomly selected non-AI genes (gray). This residual inflation could be explained if some of these genes have real eQTLs in the absence of AI or if they have AI effects that merely miss statistical significance. We generated subsets of SNPs overlapping erythroid enhancers (blue), GATA1 and TAL1 ChIP-seq peaks inside erythroid enhancers (purple), GATA1- or GATA1-TAL1–binding motifs inside erythroid enhancers (red and yellow, respectively), or all GATA1- or GATA1-TAL1–binding motifs (light and dark green, respectively). These subsets of variants show substantial enrichment (as summarized by the λGC statistic) when compared with all SNPs (black). (C) Manhattan plot of eQTL P values. The dashed line corresponds to FDR q value = 0.05. (D) Number of genes that share at least 1 eQTL between erythroblasts and the GTEx tissues (at P < 0.001). The dashed line corresponds to the mean percentage of shared eGenes (mean = 20.8%).