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Charting the cis-regulome of activated B cells by coupling structural and functional genomics

Nature Immunology volume 21, pages 210–220 (2020)Cite this article

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Abstract

Cis-regulomes underlying immune-cell-specific genomic states have been extensively analyzed by structure-based chromatin profiling. By coupling such approaches with a high-throughput enhancer screen (self-transcribing active regulatory region sequencing (STARR–seq)), we assembled a functional cis-regulome for lipopolysaccharide-activated B cells. Functional enhancers, in contrast with accessible chromatin regions that lack enhancer activity, were enriched for enhancer RNAs (eRNAs) and preferentially interacted in vivo with B cell lineage-determining transcription factors. Interestingly, preferential combinatorial binding by these transcription factors was not associated with differential enrichment of their sites. Instead, active enhancers were resolved by principal component analysis (PCA) from all accessible regions by co-varying transcription factor motif scores involving a distinct set of signaling-induced transcription factors. High-resolution chromosome conformation capture (Hi-C) analysis revealed multiplex, activated enhancer–promoter configurations encompassing numerous multi-enhancer genes and multi-genic enhancers engaged in the control of divergent molecular pathways. Motif analysis of pathway-specific enhancers provides a catalog of diverse transcription factor codes for biological processes encompassing B cell activation, cycling and differentiation.

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Fig. 1: A functionally filtered cis-regulome for activated B cells.
Fig. 2: Preferential combinatorial interactions of B cell fate-determining transcription factors with a functional cis-regulome.
Fig. 3: Transcription factor motif signatures that distinguish the functional cis-regulome.
Fig. 4: B cell enhancers are densely connected with active promoters in the functional cis-regulome.
Fig. 5: Graded activity states of multi-enhancer genes and multi-genic enhancers.
Fig. 6: Diverse transcription factor combinatorial codes controlling distinct molecular pathways in activated B cells.

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Data and accession

Data is submitted to GEO and can be accessed using the accession number GSE121753.

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Acknowledgements

This study was supported by funds from the Cincinnati Children’s Research Foundation (CCRF) to H.S. We thank the core facilities of the Cincinnati Children’s Hospital Medical Center for assistance with mouse colony management, flow cytometry and high-throughput DNA sequencing. We thank M. Haas (CCHMC) for advice with FAIRE–seq and D. Fletcher (CCHMC) for technical support in sequencing of STARR–seq and eRNA libraries. The genomic analyses were made possible through use of the CCHMC BMI computing cluster and the Center for Research Computing at the University of Pittsburgh (PA, USA). We are particularly grateful to the following colleagues at CCHMC: L. Grimes, M.Weirauch, E. Miraldi and S. Pasare for useful scientific discussions and critical input on the manuscript.

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  1. Virendra K. Chaudhri & Harinder Singh

    Present address: Center for Systems Immunology and the Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA

Authors and Affiliations

  1. Division of Immunobiology and Center for Systems Immunology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Virendra K. Chaudhri, Krista Dienger-Stambaugh, Zhiguo Wu, Mahesh Shrestha & Harinder Singh

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Contributions

V.K.C. and H.S. conceptualized the study and designed the experiments. V.K.C. performed FAIRE–seq, STARR–seq, Hi-C, nascent RNA–seq and all computational genomic analyses. K.D-S. performed RNA–seq and Z.W. performed histone ChIP–seq. M.S. and V.K.C. pursued the transfection experiments with luciferase reporters in CH12 cells. V.K.C. and H.S. interpreted the results and wrote the manuscript. H.S. supervised the project and is responsible for funding.

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Correspondence to Harinder Singh.

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Peer review information L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Reproducibility of RNA-seq, chromatin profiling and STARR-seq in LPS activated B-cells.

Analyzed data is from biological replicates. a, RNA-seq data is plotted for all known genes as log transform of FPKM values. b, FAIRE-seq peaks are plotted as quantile normalized tag counts for 55,133 regions c,d. H3K27ac peaks (n=23,938) and H3K4me3 peaks (n=16,871) plotted for quantile normalized tag counts, respectively. e,f. A pool of size-selected FAIRE-DNA fragments were used to construct two libraries, each of which was transfected into LPS activated splenic B cells at either 48h or 60h. RNA was isolated at 72h post-activation for sequencing. Quantile normalized tag counts of STARR-seq output for the 55,130 FAIRE-seq peaks are plotted. In all panels, a value of 1 was added to the counts before transforming to log2. Indicated PCC values are Pearson correlation coefficients for biological replicates. g, Levels of nascent transcripts from genes whose promoters are situated within 2 Mb intervals around STARR-pos or STARR-neg regions used in luciferase assay. Indicated p-value is based on a two-sided t-test involving genes in STARR-pos (n=762) and STARR-neg (n=377) sets.

Extended Data Fig. 2 Combinatorial binding of B cell determining TFs across genome.

a, Combinatorial binding of B-cell TFs was analyzed using the ChIP-seq peaks for the 6 TFs, Ebf1, Pax5, E2A, PU.1, Blimp-1 and Ets1. Peaks were binned based on how many represented singular binding events versus those that evidenced binding by 2 or more of the six TFs. b, Assessment of TF ChIP-seq bins analyzed in panel a for the co-occurrence of TF binding motifs of the same TFs. Motif co-occurrence frequency (horizontal axis) across each TF ChIP-seq bin (vertical axis) is plotted as a heatmap. c, FAIRE-seq peak tag counts for each of the bins analyzed. FAIRE-seq peak counts in each bin were as follows; bin1=10,500, bin2=7,224, bin3=5,997, bin4=5,602, bin5=3,795 and bin6=1,014. Boxplots display median values (notches) along with 95% confidence intervals. d, STARR-seq peak tag counts for each of the indicated ChIP-seq bins. STARR-seq peak counts in each bin were as follows; bin1=1,615, bin2=1,596, bin3=1,899, bin4=2,317, bin5=1,952 and bin6=628. e. STARR-positive and STARR-negative regions were analyzed for TF binding peaks for indicated B lineage determining TFs and scanned for their motifs, shown in Fig. 2a. The ratios of fractions of STARR-pos (n=11,809) and STARR-neg (11,942) sequences overlapping the TF binding peaks (violet bars) or containing the indicated TF motifs (turquoise bars) are plotted. Paired two-sided t-test comparing TF binding versus TF motif occurrence, P=0.0009. f. The regions in panel S3A were further analyzed for motif combinations. Percentages of STARR-pos or STARR-neg sequences with 1-6 TF motifs are plotted. Boxplots in panels c, d display median values along with the inter-quartile distributions.

Extended Data Fig. 3 PC1 and PC2 loadings of FAIRE-seq regions using mscore matrix.

Key TF motifs that contribute substantially to the variance explained by PC1 and PC2 are displayed on the plot. Highlighted TF motifs with their mscore distributions in the STARR-pos (n=11,809) versus STARR-neg (11,942) regions are displayed in boxplots with indicated p-values for two-sided Welch 2-sample test. Boxplots for selected motifs display median values of mscores along with the inter-quartile distributions.

Extended Data Fig. 4 Hi-C analysis and connectivity of functional B cell cis-regulome.

Hi-C replicates of LPS activated B cells (72h). Biologically independent experiments were analyzed for reproducibility by bin-wise genome wide correlation for the indicated bin-sizes at ~300 million read depths for each replicate. The Homer function getHiCcorrDiff.pl was used to generate correlation matrices. Correlation coefficients of each bin at a given resolution are plotted. Violin plots show kernel probability density of the data at the indicated levels of Hi-C resolution. Number of genomic bins at a given level of resolution were as follows; 10kb n= 258,640, 20kb n= 129,308, 5kb n=295,287 and 50kb n=51,678.

Extended Data Fig. 5 Histone profiles of multi-genic enhancers.

Genome browser plots of H3K27ac and H3K4me3 peaks at indicated multi-genic enhancers in resting (rB) and activated (aB) B cells. P10 and P11 refer to the numbers of interacting promoters for indicated enhancer.

Extended Data Fig. 6 Genes and TF motif enrichment analysis in activated B cells.

a, KEGG pathway enrichment using GSEA with RNA-seq data from resting (rB) and LPS activated B cells (aB), n=2 biological replicates. Enrichment plots for indicated KEGG terms are displayed b, TF motif enrichment analysis (HOMER) was performed using STARR-seq tags for peaks that were uniquely present in the 48h (n=1,851) or 60h (n=2,869) transfection datasets. TF motif enrichment values (log2 odds ratios) were determined for each transfection time point using the shared peaks for both time points as the background set. In the plotted data, TF motifs that were enriched with logP values < -20 (HOMER output) are indicated as colored circles. c, TF motif enrichment analysis (HOMER) was performed using STARR-seq tags for peaks that were uniquely present in the 48h transfection dataset using the 60h dataset as background or vice versa (see panel b). TFs whose motifs (simple or composite elements) are highly enriched (logP < -20) are displayed in the bar plot with their log2 odds ratios.

Extended Data Fig. 7 PCA-DA analysis of monogenic PC and GC enhancers.

PC1 and PC2 loadings for all TF motifs constituting the mscore matrix are displayed corresponding to the PCA plot in Fig. 5d. Motif names are displayed instead of dots. Where a single motif represents the binding site of 2 or more related TFs, TF names are separated by “:” to indicate all TFs that recognize that motif. The dashed line delineates TF motifs whose mscore variance distinguishes the PC (n=247) and GC (n=228) enhancers.

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Chaudhri, V.K., Dienger-Stambaugh, K., Wu, Z. et al. Charting the cis-regulome of activated B cells by coupling structural and functional genomics. Nat Immunol 21, 210–220 (2020). https://doi.org/10.1038/s41590-019-0565-0

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