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High-throughput mapping of regulatory DNA

Abstract

Quantifying the effects of cis-regulatory DNA on gene expression is a major challenge. Here, we present the multiplexed editing regulatory assay (MERA), a high-throughput CRISPR-Cas9–based approach that analyzes the functional impact of the regulatory genome in its native context. MERA tiles thousands of mutations across 40 kb of cis-regulatory genomic space and uses knock-in green fluorescent protein (GFP) reporters to read out gene activity. Using this approach, we obtain quantitative information on the contribution of cis-regulatory regions to gene expression. We identify proximal and distal regulatory elements necessary for expression of four embryonic stem cell–specific genes. We show a consistent contribution of neighboring gene promoters to gene expression and identify unmarked regulatory elements (UREs) that control gene expression but do not have typical enhancer epigenetic or chromatin features. We compare thousands of functional and nonfunctional genotypes at a genomic location and identify the base pair–resolution functional motifs of regulatory elements.

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Figure 1: Multiplexed editing regulatory assay (MERA).
Figure 2: MERA enables systematic identification of required cis-regulatory elements for Tdgf1.
Figure 3: MERA enables systematic identification of required cis-regulatory elements for Zfp42.
Figure 4: Functional motif discovery analysis of region-specific mutant genotypes at enhancers reveals required regulatory motifs.
Figure 5: Functional motif discovery analysis of a URE reveals critical base positions involved in gene regulation.
Figure 6: Local genotypes at an enhancer and a URE dictate Tdgf1 expression phenotype.

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Acknowledgements

The authors thank F. Zhang (Broad Institute of MIT and Harvard) for reagents, the MIT BiomicroCenter for high-throughput sequencing assistance, and Y. Qiu for flow cytometric assistance. The authors acknowledge funding from the US National Institutes of Health to D.K.G. (1U01HG007037) and to R.I.S. (1K01DK101684-01) and Harvard Stem Cell Institute's Sternlicht Director's Fund award, Brigham and Women's Hospital BRI Innovation Fund, and Human Frontier Science Program grants to R.I.S.

Author information

Authors and Affiliations

Authors

Contributions

Experiments were designed by N.R., R.I.S. and D.K.G. MERA experiments were performed by R.S., S.S., K.K., B.B. and B.J.M.E. N.R. and D.K.G. performed the computational analysis. Y.G., T.S. and M.D.E. helped with the computational analysis.

Corresponding authors

Correspondence to David K Gifford or Richard I Sherwood.

Ethics declarations

Competing interests

A patent application on MERA has been filed by the authors’ institutions.

Integrated supplementary information

Supplementary Figure 1 Effect of the length of homology arms of guide RNA on background cutting due to unintegrated guide RNA PCR fragments.

Homology constructs with a GFP-targeting gRNA were introduced into the cell. In the absence of a dummy-cleaving guide RNA, the homology construct would not be integrated into the ROSA locus, hence any loss of GFP in the cell would be due to this unintegrated construct cutting the target sequence in the GFP gene. Thus, we were able to measure the effects of different lengths of homology arms as percentage GFP-loss due to cutting the target site by unintegrated guide RNA.

Supplementary Figure 2 CRISPR-Cas9–mediated mutation following homologous recombination into a genomically integrated gRNA cassette.

Tdgf1GFP mESCs with a ROSA26-integrated U6 promoter dummy gRNA expression cassette express uniformly strong GFP. After electroporation of Cas9, dummy gRNA-targeting gRNA plasmid, and a PCR fragment comprising a gRNA targeting GFP flanked by 120-140bp homology arms,30% of cells lose GFP expression. Omission of the dummy gRNA-targeting gRNA plasmid results in minimal GFP loss, showing that homologous recombination of the PCR fragment is required for proper gRNA targeting.

Supplementary Figure 3 MERA followed by flow cytometry enables isolation of GFP cells at four mESC loci.

NanogGFP fusion, Rpp25GFP fusion, and Tdgf1GFP mESCs express GFP. After bulk gRNA integration followed by flow cytometry, highly enriched GFPmedium/neg populations can be purified. These populations are then deep sequenced.

Supplementary Figure 4 Predicting enrichment of gRNAs in GFPneg or GFPmedium populations.

a.) Correlation between bulk reads at all integrated gRNAs in two biological replicates for the NanogGFP line. b.) Reads in GFPneg population are highly correlated with the bulk reads per GFP-targeting gRNA in a particular replicate of the Tdgf1 population. c.) Reads in GFPmedium population are correlated with the bulk reads per GFP-targeting gRNA in a particular replicate of the Tdgf1 population. d,e.) Distribution of the log10 ratio of GFPneg to bulk reads for all integrated gRNAs. Blue bars indicate gRNAs not significantly enriched for GFP-loss while red bars indicate the gRNAs predicted as significant. Black asterisks show the position of the GFP-targeting gRNAs on the x-axis and tend to be towards thr far-right. Black dot shows the position of the dummy gRNA on the x-axis.As examples, the distribution is shown for d.)Tdgf1 Replicate 1 e.) Zfp42 Replicate 2. f.) The distribution of gRNAs in a 1kb window centered at the Tdgf1 promoter. Black indicates gRNAs that are integrated but do not cause any significant GFP-loss, red is for gRNAs that are significantly enriched in GFPneg population, and green is for gRNAs significantly enriched in GFPmedium population.

Supplementary Figure 5 MERA enables systematic identification of required cis-regulatory elements and their relative importance irrespective of putative off-target effects of a few individual guide RNAs in Tdgf1GFP.

a.) A genomic view of the gRNA distribution along TDGF1proximal regulatory region showing reads for individual gRNAs for a replicate before and after filtering for guide RNAs with off-target effects., DNase-I hotspot regions, predicted enhancers (green=weak, red=strong),transcription factor binding density based on ChIP-seq data and histone modifications. Predicted off-target cutting sites for each guide RNA is also shown as a panel (black).Guide RNAs redicted to cause significant GFP-loss upon introduction into the Zfp42 GFP line(red) are seen to be much fewer and not clustered as in the Tdgf1 library.b,c.) Guide RNAs enriched for GFP-loss at the b.) external promoter Lrrc2 and c.)unmarked regulatory regonare shown before and after filtering for off-target effects.

Supplementary Figure 6 MERA enables systematic identification of required cis-regulatory elements and their relative importance irrespective of putative off-target effects of a few individual guide RNAs in Zfp42GFP.

a.) A genomic view of the gRNA distribution along the Zfp42 proximal regulatory region showing reads for individual gRNAs before and after filtering for guide RNAs with off-target effects, DNase-I hotspot regions, predicted enhancers (green=weak, red=strong),transcription factor binding density based on ChIP-seq data and histone modifications. Predicted off-target cutting sites for each guide RNA is also shown as a panel (black). Guide RNAs predicted to cause significant GFP-loss upon introduction into the Tdgf1 GFP line(red) are seen to be much fewer and not clustered as in the Zfp42 library. b.) The Trim12 promoter >150kb away from the Zfp42 gene shows clusters of guide RNAs significantly enriched in GFPneg and GFPmedium populations even after filtering for off-target effects. c.) Relative importance of various fuctional categories (Figure 3d) as measured by fraction of GFPneg enriched gRNAs is invariant upon filtering out gRNAs with off-target effects.

Supplementary Figure 7 Deriving rules for off-target prediction using the GUIDE-seq assay and MERA-generated data.

a. Fraction of off-target sequences predicted from GUIDE-seq(total==442, number of guide RNAs=13) with a particular number of mismatches in non-seed(1-8bp),seed(9-20bp) or PAM sequence.Maximum of 4 mismatches in the seed and non-seed sequence and a maxmimum of one mismatch in the PAM (NNG/NGN) can be tolerated. b. Fraction of GUIDE-seq derived off-target sequences containing 3 adjacent mismatches along the bases of the guide RNA. For a NGG PAM sequence, no triple mismatches are tolerated in the seed region and for NGN/NNG PAM sequence, no triple mismatches are tolerated beyond the fifth base of the gRNA. c. Total number of mismatches tolerated is proportional to the total GC content of the guide RNA sequence. For gRNAs with intermediate GC content (10 to 15), seed GC content determines mismatches tolerated. d.True positive rate for rules of off-target prediction with or without GC content adjustment evaluated as percentage of accurately predicted off-target sites per guide RNA. e.False positive rate for rules of off-target prediction with or without GC content adjustment evaluated as number of number of guide RNAs observed to have no enrichment in GFPneg population with a predicted off-target effect on a gRNA significantly enriched in the GFPneg population. Analysis shown within the same library in Tdgf1 and Zfp42 and also cross-library situations.

Supplementary Figure 8 Effect of introducing a mismatched gRNA library on GFP loss for a particular gene.

Introduction of sgTdgf1 library into the Tdgf1GFP line, Introduction of sgZfp42 library into the Tdgf1GFP line, introduction of sgTdgf1 library into the Zfp42GFP line, Introduction of sgZfp42 library into the Zfp42GFP line (from left to right clockwise).

Supplementary Figure 9 Comparison of cis-regulatory programs across two genes, Nanog and Rpp25.

a,b) A genomic view of the gRNAs designed for various regions expected to be involved in regulation of Nanog including two distal regions predicted from PolII Chia-Pet data. b.) A genomic view of the gRNAs designed for various regions expected to be involved in regulation of Rpp25 including two distal regions predicted from PolII Chia-Pet data. c,d) Fraction of significant gRNA among the different functional genomic categories involved in the regulation of c) Nanog, and d) Rpp25.

Supplementary Figure 10 Distribution of start and end positions of contiguous mutations or “disruptions” of various lengths along the sequenced read.

a-d.) Left and right ends of mutations caused by a gRNA along the length of a read are plotted along the y and x-axes respectively. Each point is a set of genotypes with that particular position of the ends of the disruption in. Ratio of GFPneg to GFPpos reads corresponding to a particular point is shown as blue to bluish-yellow (<1, GFPpos biased), or yellow to red (>1, GFPneg biased). gRNAs are shown as black rectangles along the axes and their boundaries are indicated by thin black lines. Thick black boundaries show region within -20 to +20bp selected for further analysis and classification. Disruptions are shown for a.) Left paired end read for Tdgf1 proximal enhancer with two gRNAs,.b) Right paired end read for Tdgf1 proximal enhancer with two gRNAs. c.)Left paired end read for Zfp42 enhancer with a single gRNA, d.)Right pared end read for Zfp42 enhancer with a single gRNA.

Supplementary Figure 11 Deep sequencing of two gRNAs within a Tdgf1 proximal enhancer region validates their role in regulation of Tdgf1 and reveals patterns of mutation and functional motifs in the region.

a,b.)Fraction of unique genotypes in GFPneg and GFPpos populations with a mutations at bases along the gRNAs reveal pattern of cleavage around the gRNA for c.)Left paired end read. d.)Right paired end read. c,d.)Fraction of unique genotypes in GFPneg and GFPpos with insertion between bases along the gRNA for c.)Left paired end read, d.)Right paired end read. e.)Motif logo for region mutated by gRNAs with base scores computed as log-ratios of the hellinger distance of the GFPneg genotypes at a base to the reference base to the hellinger distance of the GFPpos genotypes at a base to the reference base caused by Tdgf_gRNA_1 and Tdgf_gRNA_2 in the right paired end read. f,g.)Motif logo for insertions showing entropic gain upon GFP-loss in interevening base positions in f.) Left paired end read, g.) Right paired end read.

Supplementary Figure 12 Deep sequencing of two gRNAs within a Zfp42 enhancer region validates their role in regulation of Zfp42 and reveals functional motifs associated with gene activity.

a.)Two gRNAs at a Zfp42 enhancer region in the genomic context showing its overlap with DNAse-I hotspot and predicted enhancer regions and transcription factor binding sites. b,c.) ROC curve for 5-fold classification of GFPneg and GFPpos genotypes using mutations within -20 to +20bp of the gRNA as features for b.) Zfp_gRNA_1 using mutations on the left paired end read, c.)Zfp_gRNA_2 using mutations on the right paired end read.Unweighted classification (in blue) counts each unique genotype in the test-set only once while weighted classification(red) calculates sensitivity and specificity counting each unique genotype in the test-set based as many times as the number of reads assigned to it. d,e.)Motif logo for region mutated by gRNAs with base scores computed as log-ratios of the hellinger distance of the GFPneg genotypes at a base to the reference base to the hellinger distance of the GFPpos genotypes at a base to the reference base, d.) Left paired end read with Zfp_gRNA_1, e.) Right paired end read with Zfp_gRNA_2.

Supplementary Figure 13 Deep sequencing of two gRNAs within a Zfp42 enhancer region reveals differences in mutational spectrum associated with loss of gene expression.

a,b.)Fraction of unique genotypes in GFPneg and GFPpos populations with a mutations at bases along the gRNAs reveal pattern of cleavage around the gRNA for a.)Left paired end read. b.)Right paired end read. c,d.)Fraction of unique genotypes in GFPneg and GFPpos with insertion between bases along the gRNA for c.)Left paired end read, d.)Right paired end read. e,f.)Motif logo for insertions showing entropic gain upon GFP-loss in interevening base positions in e.) Left paired end read, f.) Right paired end read.

Supplementary Figure 14 Deep sequencing of two gRNAs within a Tdgf1 URE validates its role in regulation of Tdgf1 and reveals patterns of mutation and functional motifs in the region.

a,b.) Left and right ends of mutations caused by a gRNA along the length of a read are plotted along the y and x-axes respectively. Each point is a set of genotypes with that particular position of the ends of the disruption in. Ratio of GFPneg to GFPpos reads corresponding to a particular point is shown as blue to bluish-yellow (<1, GFPpos biased), or yellow to red (>1, GFPneg biased). gRNAs are shown as black rectangles along the axes and their boundaries are indicated by thin black lines. Thick black boundaries show region within -20 to +20bp selected for further analysis and classification. Disruptions are shown for a.) Left paired end read for Tdgf1 URE with a single gRNA,.b) Right paired end read for Tdgf1 URE with another gRNA. c,d.)Fraction of unique genotypes in GFPneg and GFPpos with insertion between bases along the gRNA for c.) ROC curve for 5-fold classification of GFPneg and GFPpos genotypes using mutations on the left paired end read within -20 to +20bp of Tdgf_URE_gRNA1.Unweighted classification (in blue) counts each unique genotype in the test-set only once while weighted classification(red) counts each unique genotype in the test-set as many times as the number of reads assigned to it, for calculating sensitivity and specificity. d.) Fraction of unique genotypes in GFPneg and GFPpos populations with mutations along the left paired end read within -20/+20bp of the Tdgf_URE_gRNA1 reveal the pattern of cleavage. e.) Motif logo for region mutated by gRNAs with base scores computed as log-ratios of the hellinger distance of the GFPneg genotypes at a base to the reference base to the hellinger distance of the GFPpos genotypes at a base to the reference base for the left paired end read containing Tdgf-URE_gRNA1.

Supplementary Figure 15 Deep sequencing of two gRNAs within a Tdgf1 URE validates it’s role in regulation of Tdgf1 and reveals patterns of mutation and functional motifs in the region.

a,b.) Fraction of unique genotypes in GFPneg and GFPpos with insertions between bases along the gRNA for a.) Left paired end read, b.) Right paired end read. c,d.) Motif logo for insertions showing entropic gain upon GFP-loss in intervening base positions in c.) Left paired end read, d.) Right paired end read. e).Vertebrate Phastcons score along bases of Tdgf_URE_gRNA1 reveal highly conserved left half of the sequence.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15, Supplementary Discussion, Supplementary Methods and Supplementary Tables 5–7 (PDF 5418 kb)

Supplementary Table 1

Genomic locations targetted by gRNAS in the Tdfg1 library and sequenced read counts per gRNA from bulk, GFP-negative and GFP-medium populations (XLSX 258 kb)

Supplementary Table 2

Genomic locations targetted by gRNAS in the Zfp42 library and sequenced read counts per gRNA from bulk, GFP-negative and GFP-medium populations (XLSX 271 kb)

Supplementary Table 3

Genomic locations targetted by gRNAS in the Nanog library and sequenced read counts per gRNA from bulk and GFP-negative populations (XLSX 173 kb)

Supplementary Table 4

Genomic locations targetted by gRNAS in the Rpp25 library and sequenced read counts per gRNA from bulk and GFP-negative populations (XLSX 101 kb)

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Rajagopal, N., Srinivasan, S., Kooshesh, K. et al. High-throughput mapping of regulatory DNA. Nat Biotechnol 34, 167–174 (2016). https://doi.org/10.1038/nbt.3468

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