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Systematic discovery and perturbation of regulatory genes in human T cells reveals the architecture of immune networks

Nature Genetics volume 54, pages 1133–1144 (2022)Cite this article

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Abstract

Gene regulatory networks ensure that important genes are expressed at precise levels. When gene expression is sufficiently perturbed, it can lead to disease. To understand how gene expression disruptions percolate through a network, we must first map connections between regulatory genes and their downstream targets. However, we lack comprehensive knowledge of the upstream regulators of most genes. Here, we developed an approach for systematic discovery of upstream regulators of critical immune factors—IL2RA, IL-2 and CTLA4—in primary human T cells. Then, we mapped the network of the target genes of these regulators and putative cis-regulatory elements using CRISPR perturbations, RNA-seq and ATAC-seq. These regulators form densely interconnected networks with extensive feedback loops. Furthermore, this network is enriched for immune-associated disease variants and genes. These results provide insight into how immune-associated disease genes are regulated in T cells and broader principles about the structure of human gene regulatory networks.

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Fig. 1: Approach to map disease gene networks in human T cells.
Fig. 2: Discovery of upstream regulators of IL2RA, IL-2 and CTLA4.
Fig. 3: Arrayed knockouts validate and characterize screen results.
Fig. 4: Individual regulators act at distinct IL2RA CREs.
Fig. 5: IL2RA regulators form highly interconnected gene networks.
Fig. 6: Coregulated gene sets are enriched for immune disease genes.
Fig. 7: IL2RA regulators affect CREs and genes associated with multiple sclerosis.

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Data availability

The raw sequencing files generated during this study are available at GEO under accession GSE171737. Transcription factor binding motifs used in this study were downloaded from JASPAR2020 (https://doi.org/10.18129/B9.bioc.JASPAR2020), HOCOMOCO v.11 (https://hocomoco11.autosome.org/) and CIS-BP (http://cisbp.ccbr.utoronto.ca/index.php).

Code availability

The code for this paper is available at https://doi.org/10.5281/zenodo.637164689.

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Acknowledgements

We thank members of the Marson and Pritchard laboratories for helpful discussions and manuscript feedback. We thank H. Pimentel for advice on analysis. This research was supported by National Institutes of Health (NIH) grants R01HG008140 (J.K.P.) and RM1-HG007735 (W.J.G.). A.M. held a Career Award for Medical Scientists from the Burroughs Wellcome Fund, is a member of the Parker Institute for Cancer Immunotherapy (PICI), was an investigator at the Chan Zuckerberg Biohub and has received funding from the Innovative Genomics Institute (IGI), the American Endowment Foundation, the Cancer Research Institute (CRI) Lloyd J. Old STAR award, a gift from the Jordan Family, a gift from the Byers family and a gift from B. Bakar. O.S. was supported by the NIH grant T32AI125222. S.N. was supported by a Helen Hay Whitney Fellowship. N.S.-A. was supported by a Stanford Graduate Fellowship and CEHG Fellowship. A.F.C. was supported by an NIH F32 postdoctoral fellowship (5F32GM135996-02). Sorting was carried out at the UCSF Flow Cytometry Core (RRID:SCR_018206) supported in part by NIH grant P30 DK063720 and by the NIH S10 instrumentation grant S10 1S10OD021822-01 and the Gladstone Flow Cytometry Core supported by the James B. Pendleton Charitable Trust. RNA-seq was carried out at the DNA Technologies and Expression Analysis Cores at the UC Davis Genome Center, supported by NIH Shared Instrumentation Grant 1S10OD010786-01. Other sequencing was carried out at the UCSF CAT, supported by a PBBR grant. Some of the computing for this project was performed on the Sherlock cluster. We thank Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results.

Author information

Author notes

  1. These authors contributed equally: Jacob W. Freimer, Oren Shaked.

  2. These authors jointly supervised this work: Jonathan K. Pritchard, Alexander Marson.

Authors and Affiliations

  1. Department of Genetics, Stanford University, Stanford, CA, USA

    Jacob W. Freimer, Sahin Naqvi, Nasa Sinnott-Armstrong, Arwa Kathiria, Amy F. Chen, William J. Greenleaf & Jonathan K. Pritchard

  2. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA

    Jacob W. Freimer, Christian M. Garrido & Alexander Marson

  3. Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA

    Jacob W. Freimer, Oren Shaked, Jessica T. Cortez & Alexander Marson

  4. Department of Surgery, University of California, San Francisco, CA, USA

    Oren Shaked

  5. Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, USA

    Sahin Naqvi

  6. Department of Applied Physics, Stanford University, Stanford, CA, USA

    William J. Greenleaf

  7. Chan Zuckerberg Biohub, San Francisco, CA, USA

    William J. Greenleaf & Alexander Marson

  8. Department of Biology, Stanford University, Stanford, CA, USA

    Jonathan K. Pritchard

  9. Department of Medicine, University of California San Francisco, San Francisco, CA, USA

    Alexander Marson

  10. Diabetes Center, University of California San Francisco, San Francisco, CA, USA

    Alexander Marson

  11. Innovative Genomics Institute, University of California Berkeley, Berkeley, CA, USA

    Alexander Marson

  12. UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA

    Alexander Marson

  13. Parker Institute for Cancer Immunotherapy, University of California San Francisco, San Francisco, CA, USA

    Alexander Marson

  14. Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA

    Alexander Marson

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  1. Jacob W. Freimer
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Contributions

J.W.F., O.S., J.K.P. and A.M. conceptualized the study. J.W.F., S.N. and N.S.-A. performed the formal analysis. J.W.F., O.S., A.K., C.M.G., A.F.C. and J.T.C. performed the investigations. W.J.G., J.K.P. and A.M. provided resources. J.W.F. wrote the original draft of the manuscript. J.W.F., O.S., J.K.P. and A.M. reviewed and edited the manuscript. J.W.F. performed visualization. W.J.G., J.K.P. and A.M. supervised the study. W.J.G., J.K.P. and A.M. acquired funding.

Corresponding authors

Correspondence to Jonathan K. Pritchard or Alexander Marson.

Ethics declarations

Competing interests

A.M. is a compensated cofounder, member of the boards of directors and a member of the scientific advisory boards of Spotlight Therapeutics and Arsenal Biosciences. A.M. is a cofounder, member of the boards of directors and a member of the scientific advisory board of Survey Genomics. A.M. is a compensated member of the scientific advisory board of NewLimit. A.M. owns stock in Arsenal Biosciences, Spotlight Therapeutics, NewLimit, Survey Genomics, PACT Pharma and Merck. A.M. has received fees from 23andMe, PACT Pharma, Juno Therapeutics, Trizell, Vertex, Merck, Amgen, Genentech, AlphaSights, Rupert Case Management, Bernstein and ALDA. A.M. is an investor in and informal advisor to Offline Ventures and a client of EPIQ. The Marson laboratory has received research support from Juno Therapeutics, Epinomics, Sanofi, GlaxoSmithKline, Gilead and Anthem. W.J.G. is a consultant for 10x Genomics, which has licensed IP associated with ATAC-seq. W.J.G. has additional affiliations with Guardant Health (consultant) and Protillion Biosciences (cofounder and consultant). J.W.F. is a consultant for NewLimit. J.W.F., O.S., J.K.P. and A.M. are listed as inventors on a patent application related to this work. The remaining authors declare no competing interests.

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Nature Genetics thanks Rahul Roychoudhuri, Andrew Wells and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Quality control of the CRISPR screens.

a, Fluorescence activated cell sorting (FACS) gating for IL2RA, IL-2, and CTLA4 screens. Representative example from the IL2RA screen is shown. b, Abundance of sgRNAs targeting GFP in either the starting plasmid or in the GFP + sorted population (n = 3 donors, 1 plasmid pool). c, Differential enrichment between the high- and low-expression bins for sgRNAs targeting genes that are either expressed or not expressed in CD4 + T cells based on RNA-Seq. d, Abundance of sgRNAs targeting essential genes, fitness genes, non-essential genes, or non-targeting guides in the starting plasmid (n = 1) or in the GFP + sorted samples (n = 3 donors). e, Enrichment of sgRNAs between the GFP + sorted population and starting plasmid. Results from Donor 1 and Donor 2 are depicted. Significant hits were identified with MAGeCK and genes with an FDR-adjusted P < 0.05 across all donors are highlighted. f, Comparison of the number of shared significant hits between the different screens and whether those hits have the same direction of effect on their targets. Two-sided sign test P = 0.002, shared direction of effect = 82%, 95% confidence interval 61-95%. All boxplots show the median, first and third quartiles, and 1.5x the interquartile range.

Extended Data Fig. 2 Arrayed knockouts validate IL-2 and CTLA4 screen results.

a,b, Representative flow cytometry density plots for IL-2 (a) or CTLA4 (b) protein levels after knockout of top screen hits. Knockout of hits that decrease target levels are shown in blue, and knockout of hits that increase target levels are shown in red. c,d, Summary of changes in IL-2 (c) or CTLA4 (d) levels measured using flow cytometry. Screen hits selected for validation are displayed on the y-axis ordered by their effect size in the pooled CRISPR screen. For each knockout, bars show the average change in IL-2 or CTLA4 median fluorescence intensity relative to non-targeting controls. Dots show individual data points, and error bars show standard deviation across two guide RNAs and three donors per guide RNA. Concordant changes between the screen and validation that increase or decrease IL-2/CTLA4 levels are shown in red or blue, respectively. Discordant changes are shown in grey. The average insertion/deletion (indel) percentage at the genomic target site across multiple donors for guide RNA 1 (n = 3) and guide RNA 2 (n = 2) is shown to the right. e, Representative flow cytometry density plots for IL2RA protein levels after cells are grown with exogenous IL-2 or without IL-2 + blocking anti-IL-2 antibody. f, Knockout of top regulators of IL2RA in cells cultured with exogenous IL-2 or without IL-2 + blocking anti-IL-2 antibody. IL2RA median fluorescent values are normalized to AAVS1 control knockouts with exogenous IL-2 (black dashed line). Colored dashed lines show the normalized IL2RA median fluorescent intensity averaged across the AAVS1 control knockouts without IL-2 + blocking anti-IL-2 antibody in each donor.

Extended Data Fig. 3 Downstream mapping of genes and chromatin sites controlled by each IL2RA regulator.

a, mRNA fold change for the CRISPR targeted gene in each knockout sample. Data are presented as the effect size from Limma, with error bars showing the 95% confidence interval. b, Comparison of average changes in IL2RA mRNA levels (RNA-Seq) and protein levels (flow cytometry) for each knockout sample collected for RNA-Seq and ATAC-Seq. c, Percent of significantly changed ATAC-Seq peaks in each knockout sample that contain a known motif for the knocked out transcription factor. d,e, The total number of significantly changed genes (d) or peaks (e) detected via RNA-Seq and ATAC-Seq in each knockout sample. For a-e, n = 3 donors for the RNA-Seq and ATAC-Seq data. f, Summary of changes in IL2RA levels measured using flow cytometry. For each knockout, the change in IL2RA median fluorescence intensity is normalized to AAVS1 knockout alone controls. g, The percent of reads containing insertions/deletions (indels) at the genomic target sites for the guide RNAs and samples in f. Solid line indicates the mean indel percentage across different perturbation combinations.

Extended Data Fig. 4 Direct binding of IL2RA regulators at the IL2RA locus.

Chromatin accessibility measured by ATAC-Seq in AAVS1 control knockouts is shown in black. ATAC-Seq data are shown as normalized read coverage; samples were normalized using the size factors from DESeq2. Results from previous CRISPR activation (CRISPRa) screen38 tiling the IL2RA locus in Jurkat cells is shown in pink. CRISPRa tracks show the log2 enrichment of guide RNAs in cells expressing high, mid, or low levels of IL2RA compared to background. Public ChIP-Seq data for IL2RA regulators in various subsets of human CD4 + T cells (STAT5A, STAT5B, ETS1, GATA3, MYB) or engineered bulk T cells (IRF4) are shown in green. ChIP-Seq data are shown as background subtracted binding in reads per million. ATAC-Seq peaks that were significantly differentially accessible in each knockout are shown in blue. The location of a matching binding motif in a significantly differentially accessible peak for each transcription factor is shown in orange. Where available, public ChIP-Seq tracks are from either two independent studies or individual donors: ETS148,81, GATA347, IRF482, MYB83, STAT5A and STAT5B84. chr, chromosome.

Extended Data Fig. 5 Highly co-regulated gene sets are enriched for immune disease genes.

a, Enrichment of heritability for immune traits compared to non-immune traits in significantly differentially accessible ATAC-Seq peaks for each knockout. Only knockouts with at least 1,000 significantly differentially accessible ATAC-Seq peaks are shown. b, Enrichment of heritability for immune traits compared to non-immune traits in a 100-kb window around co-regulated genes. Enrichment for matched background sets for each knockout (a) or each co-regulation bin (b) are shown. Enrichment calculated using stratified LD score regression. Traits were meta-analyzed using inverse-variance weighting; average enrichment and standard error shown. P-values were calculated by first converting the difference in average enrichments to Z-scores, and then converting Z-scores to two-sided P-values (see Methods). For a, Bonferroni-corrected P-values range from 1.8 × 10−2 to 7.5 × 10−16. For b, Bonferroni corrected P-values range from 2.7 × 10−2 to 6.6 × 10−10. NS, not significant. n = 16 immune traits and n = 15 non-immune traits for a and b.

Extended Data Fig. 6 Multiple sclerosis SNPs within CD4 + T cell ATAC-Seq peaks.

a, Enrichment of heritability in accessible ATAC-Seq peaks for different immune traits. Data are presented as estimated enrichment +/− standard error estimated from stratified LD score regression. b, The number of all protein-coding genes and differentially expressed protein-coding genes with a TSS within 100 kb of a multiple sclerosis SNP. Only high confidence multiple sclerosis SNPs (PICS probability greater than 50%) within differentially accessible ATAC-Seq peaks are shown. c, Editing outcomes in CD37 low- and high-expressing cells after using CRISPR/Cas9 and homology-directed repair templates to edit the SNP rs1465697. Editing was performed with guide RNAs targeting the CD37 CRE (CD37 guide RNA) or a control region (AAVS1 guide RNA). d, Length of deletions after CRISPR editing in CD37 low- and high-expressing cells.

Supplementary information

Supplementary Information

Supplementary Note.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–8.

Supplementary Data 1.

RNA-seq results. Results from the RNA-seq experiment following knockout of top IL2RA regulators. Data were analyzed using Limma. The results table includes the Ensembl gene id (ens_id), gene name (gene_name), log2 fold change (logFC) for knockout/control, adjusted P value (adj.P.Val) and the knockout sample that the data corresponds to (sample).

Supplementary Data 2.

ATAC-seq results. Results from the ATAC-seq experiment following knockout of top IL2RA regulators. Data were analyzed using DESeq2. The results table includes the log2 fold change (log2FoldChange) for knockout/control, adjusted P value (padj), the knockout sample that the data corresponds to (sample), the peak name (peakName) and the peak chromosomal coordinates (peak_chr, peak_start, peak_end).

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Freimer, J.W., Shaked, O., Naqvi, S. et al. Systematic discovery and perturbation of regulatory genes in human T cells reveals the architecture of immune networks. Nat Genet 54, 1133–1144 (2022). https://doi.org/10.1038/s41588-022-01106-y

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