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Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci


Genetic studies have revealed that autoimmune susceptibility variants are over-represented in memory CD4+ T cell regulatory elements1,2,3. Understanding how genetic variation affects gene expression in different T cell physiological states is essential for deciphering genetic mechanisms of autoimmunity4,5. Here, we characterized the dynamics of genetic regulatory effects at eight time points during memory CD4+ T cell activation with high-depth RNA-seq in healthy individuals. We discovered widespread, dynamic allele-specific expression across the genome, where the balance of alleles changes over time. These genes were enriched fourfold within autoimmune loci. We found pervasive dynamic regulatory effects within six HLA genes. HLA-DQB1 alleles had one of three distinct transcriptional regulatory programs. Using CRISPR–Cas9 genomic editing we demonstrated that a promoter variant is causal for T cell–specific control of HLA-DQB1 expression. Our study shows that genetic variation in cis-regulatory elements affects gene expression in a manner dependent on lymphocyte activation status, contributing to the interindividual complexity of immune responses.

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Fig. 1: Dynamic allele-specific expression during T cell activation.
Fig. 2: Dynamic allele-specific expression patterns and enrichment in autoimmune disease loci.
Fig. 3: HLA-DQB1 dynamic allele-specific expression at mRNA and protein levels.
Fig. 4: Validation of causal variant for Late-Spike cis-regulatory program.

Data availability

The RNA-seq data supporting this publication are available at GEO, with accession number GSE140244. The flow cytometry data supporting this publication are available at ImmPort ( under study accession SDY1555. Source data for Fig. 4 are provided with the paper.

Code availability

Code for key analyses in this study are publicly available in GitHub ( or upon request to the authors.


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We are indebted to G. Klein RN for her outstanding management of the Genotype and Phenotype (GaP) registry at the Feinstein Institute, to the Raychaudhuri laboratory members for critical discussions and feedback and to H. Long and P. Cejas for support on primary T cell ATAC-seq experiments. This work was supported by the National Institutes of Health (grant nos. U19AI111224, U01GM092691, U01HG009379 and R01AR063759 to S.R., NHGRI T32 HG002295 to T.A.), the Swiss National Science Foundation (Early Postdoc Mobility Fellowship to M.G.-A.), the Broad Institute through the SPARC mechanism (S.R.), the Estonian Research Council (PUT1660 to T.E.) and the European Union Horizon 2020 (grant no. MP1GI18418R to T.E.). Whole-genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). WGS for ‘NHLBI TOPMed: Multi-Ethnic Study of Atherosclerosis (MESA)’ (accession no. phs001416.v1.p1) was performed at the Broad Institute of MIT and Harvard (grant no. 3U54HG003067-13S1). Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (grant no. 3R01HL-117626-02S1; contract no. HHSN268201800002I). Phenotype harmonization, data management, sample identity quality control and general study coordination were provided by the TOPMed Data Coordinating Center (grant no. 3R01HL-120393-02S1; contract no. HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. MESA and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contract nos. HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079 and UL1-TR-001420. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant no. UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant no. DK063491 to the Southern California Diabetes Endocrinology Research Center. The full authorship list for the NHLBI TOPMed consortium can be found in

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M.G.-A., Y.B. and S.R. conceived, designed and performed analyses/experiments, wrote the paper and supervised the research. J.A., S.H., Y.L., T.A. and N.T. performed analyses or experiments, interpreted data and contributed to the paper. D.A.R., J.E., A.H.J. and M.B.B. provided experimental supervision and contributed to the paper. C.N. and P.K.G. contributed to sample recruitment and results discussion. C.N. facilitated experiments. T.E., S.S.R., K.D.T. and J.I.R. contributed to data acquisition.

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Correspondence to Soumya Raychaudhuri.

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Extended data

Extended Data Fig. 1 Replication of dynamic ASE in two pilot individuals.

For two individuals, we performed full time-course replicates (from the same CD4+ memory T cell isolation batch, but independent stimulation experiment and RNA-seq library preparation). From the dynamic ASE events called significant in replicate A at 5% FDR (as explained in main text and Methods), we asked how do the P-values and betas look in replicate B. Left plots show distribution of P-values in replicate B, middle plots show correlation of betas for time, right plots show correlation of betas for time squared. a, Individual TB03072560. b, Individual TB03073798.

Extended Data Fig. 2 Replication examples of dynamic ASE in two pilot individuals.

Examples of a dynamic ASE event significant in individual TB03072560 (a) and TB03073798 (b). Shown are allelic counts for heterozygous SNP (left) and reference fraction over time (right) for replicate A (top panels) and replicate B (bottom panels).

Extended Data Fig. 3 Reproducibility of dynASE across heterozygous individuals for the same SNP.

Here we wanted to assess whether dynamic ASE replicates well in different heterozygous individuals for the same SNP. First, from the 561 dynASE events at 5% FDR we took the top 356 unique SNPs (ensuring one heterozygous individual per SNP), and then asked how do the P-values look in other heterozygous individuals for those 356 SNPs. a, Qqplot depicting the observed P-values in the other heterozygous individuals (y-axis), compared to the expected uniform distribution of P-values (x-axis). b, Next, within all 561 significant events at 5% FDR, we evaluated the correlation of betas for time (left) and time squared (right) for all pairwise combinations of heterozygous individuals for the same SNP, i.e. het1 and het2 in x and y axis labels.

Extended Data Fig. 4 DynASE examples for SNPs with two or more heterozygous individuals.

ac, Shown are gene expression levels across 24 individuals (left), and allele counts (SNP and individual indicated) and reference fraction (P-value and FDR for dynASE indicated) for heterozygous SNPs in corresponding gene.

Extended Data Fig. 5 Scheme depicting HLA allelic expression quantification with HLA-personalized genome.

In order to quantify robustly allele-specific expression in the highly polymorphic HLA genes, we first create an HLA-personalized genome per individual. We do this by inserting into the reference genome the cDNA sequences of each HLA allele as separate sequences (12 in total given that we sequenced or typed 6 HLA genes), and masking the exonic sequences corresponding to those cDNAs in chromosome 6 of the reference genome. Next, we map the RNA-seq reads to this HLA-personalized genome, we remove PCR duplicates and we count the number of uniquely mapped reads to each HLA cDNA allele.

Extended Data Fig. 6 Allelic fraction replication in HLA gene quantifications.

Allelic fraction over time for the 3 HLA class II genes (a) and 3 HLA class I genes (b), for the two pilot individuals with full time course replicates. Replicate A in black, replicate B in blue.

Extended Data Fig. 7 Principal component analysis of HLA-DQB1 allelic profiles over time.

PCA performed for 48 HLA-DQB1 allelic expression profiles of 24 individuals (log2(FPKM+1) values over time. Allelic profiles are colored by 4-digit classical HLA-DQB1 allele (a), and by the k-means cluster to which they belong (b). Average allelic expression was computed for samples with replicates. Twelve hour time point was removed because of high number of missing values. These plots depict how 4-digit alleles group near each other (a), and how PCA also captures the three distinct cis regulatory programs (Fluctuating, Constant-Low and Late-Spike) (b).

Extended Data Fig. 8 Mapping variants associated with Late-Spike haplotype.

a, r2 between Late-Spike haplotype dosage and SNPs within 1Mb of HLA-DQB1 in Estonian cohort. Orange vertical lines indicate location of HLA-DQB1. Dots that are colored pink are intragenic SNPs in HLA-DQB1, HLA-DRB1, and HLA-DQA1. Right plot is zoomed in on HLA-DQB1 region to show top SNPs (reference genome hg19). b, HLA-DQB1 gene expression levels (log2(FPKM+1)) at 72 hours after stimulation for individuals separated by their rs71542466 genotype. c, Same as in (a) but in European MESA cohort (reference genome GRCh38). d, r2 comparison between Estonian and European MESA cohort, for all SNPs in the region (left) or the subset of SNPs in the regions that do not overlap HLA-DQB1, HLA-DRB1 or HLA-DQA1 start-end genomic coordinates (right). The 6 intergenic SNPs with top r2 in Estonians are highlighted, with 3 of them having top r2 in the European MESA cohort too. Identity line marked. These results show that our top candidate SNP rs71542466 (and the other candidate SNPs) tracks well with the Late-Spike haplotype in both the Estonian and the MESA cohort of individuals of European ancestry recruited in the United States.

Extended Data Fig. 9 Genomic location of nearest gRNAs to tested causal SNPs and representative flow cytometry plot of CRISPR-Cas9 edited HH cells.

a, Location of SNPs (red) is shown in reference to the nearest exon (blue) both upstream and downstream of HLA-DQB1. The nearest gRNA sequences used for targeting the regions are highlighted with their corresponding colors (rs71542466 - dark green, rs71542467 - light purple, rs71542468 - purple, rs72844401 - beige/orange, rs4279477 - blue, rs28451423 - light green). Alignments were plotted using SnapGene(v3.2.1). b, Representative staining of HLA-DQ on CRISPR-Cas9 modified HH cells. Cells were modified with proximal gRNA as shown in (a) and labelled accordingly. Cells stained 7-10 days after modification with HLA-DQ antibodies as a bulk population.

Extended Data Fig. 10 Sanger sequencing alignment of HH reference and base-edited clones reveal seamless editing.

Genomic DNA from expanded clones was sequenced and aligned to the reference (hg38) and visualized using SnapGene(v3.2.1). Red colored nucleotide indicates the location of the rs71542466 SNP in the reference. Highlighted red nucleotides indicate mismatches from the reference and yellow colored nucleotides indicate unresolved/heterozygous sequences.

Supplementary information

Supplementary Information

Supplementary Figs. 1–19, Note and unprocessed EMSAs from Supplementary Fig. 15

Reporting Summary

Supplementary Tables

Supplementary Table 1. Dynamic allele-specific expression for SNPs genome wide with FDR < 0.05. Supplementary Table 2. Reported eQTLs for HLA-DQB1 and LD with the Late-Spike regulatory SNP. Supplementary Table 3. Primers, probes and oligonucleotide sequences.

Source data

Source Data Fig. 4

Unprocessed EMSA from Fig. 4d.

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Gutierrez-Arcelus, M., Baglaenko, Y., Arora, J. et al. Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci. Nat Genet 52, 247–253 (2020).

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