A gene's position in the genome can profoundly affect its expression because regional differences in chromatin modulate the activity of locally acting cis-regulatory sequences (CRSs). Here we study how CRSs and regional chromatin act in concert on a genome-wide scale. We present a massively parallel reporter gene assay that measures the activities of hundreds of different CRSs, each integrated at many specific genomic locations. Although genome location strongly affected CRS activity, the relative strengths of CRSs were maintained at all chromosomal locations. The intrinsic activities of CRSs also correlated with their activities in plasmid-based assays. We explain our data with a quantitative model in which expression levels are set by independent contributions from local CRSs and the regional chromatin environment, rather than by more complex sequence- or protein-specific interactions between these two factors. The methods we present will help investigators determine when regulatory information is integrated in a modular fashion and when regulatory sequences interact in more complex ways.
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We thank S. Elgin and members of the Cohen laboratory for their critical feedback on the manuscript. We thank J. Hoisington-Lopez for assistance with high-throughput sequencing. We also thank the Alvin J. Siteman Cancer Center at Washington University School of Medicine and Barnes-Jewish Hospital in St. Louis, Missouri, for the use of the Siteman Flow Cytometry Core, which provided single-cell sorting services. The Siteman Cancer Center is supported in part by NCI Cancer Center Support Grant P30-CA91842. This work was also supported by the Hope Center Viral Vectors Core at Washington University School of Medicine and by grants to B.A.C. from the National Institutes of Health, R01-GM092910 and R01-HG008687.
The authors declare no competing financial interests.
Integrated supplementary information
Landing pads are integrated into diverse genomic locations. A variety of epigenomic features differentially mark the fifteen landing pads. Combined results from ChromHMM44 and Segway45 algorithms classify landing pads as “repressed intron”, “repressed intergenic”, “CTCF bound”, “transcribed exon” and “transcribed intron”.
Expression distributions (log2(RNA reads/DNA reads)) are plotted for high and low expressing CRS from three different ENCODE segmentation classes (R = repressed, SE = strong enhancer, WE = weak enhancer) as previously measured in 39.
(A) CRS activity was assayed in four landing pads more than two months after the initial experiment. CRS expression distributions (log2(RNA reads/DNA reads)) are plotted for each landing pad. The pattern of expression measurements is consistent with that of the initial experiment (Fig. 2B). (B) CRS are determined to have HIGH, MID, or LOW activity at landing pad 1 and plotted for each landing pad according to their classification at landing pad 1 (HIGH = orange, MID = gray, LOW = blue). The expression patterns within each landing pad are consistent with those of the main experiment (Fig. 3A).
In each panel, CRS were determined to have HIGH, MID, or LOW activity at one landing pad (reference landing pad) and plotted for each landing pad according to their classification at the landing pad noted on the X-axis (HIGH = orange, MID = gray, LOW = blue). For every landing pad, we tested whether expression distributions for HIGH, MID and LOW groups were significantly different from one another. Ø denotes two groups that were NOT significantly different as determined by a Bonferroni corrected p-value from the Wilcoxon Test (p-value > 0.05/24). According to one-way ANOVA, the three groups were different from one another at all landing pads independent of reference landing pad.
The average number of barcodes measured per CRS in every landing pad is noted in the blue box. R values for landing pad pairs with more than 10 BCs/CRS in both LPs are shaded gray. Landing pad pairs with more barcodes measured are better correlated than those with fewer barcodes measured.
(A) Fitting and testing scheme for validating the linear model. (B) Combined results from testing the model on two independent test sets (R=0.83; Rs =0.87). (C) The effect on model performance of shuffling the CRS coefficients (left; R=0.71; Rs =0.77) or (D) shuffling the landing pad coefficients (right; R=0.15; Rs =0.14).
CRS were classified as HIGH, MID, or LOW according to their activity on plasmids and their expression was plotted for each landing pad (HIGH = orange, MID = gray, LOW = blue). For every landing pad, we tested whether expression distributions for HIGH, MID and LOW groups were significantly different from one another using a Bonferroni corrected p-value from the Wilcoxon Test (p-value > 0.05/24). HIGH and LOW, and HIGH and MID groups were significantly different from one another at all landing pads except for LP8. According to one-way ANOVA, the three groups were different from one another at all landing pads.
Supplementary Figures 1–7 (PDF 1399 kb)
Landing pad locations and annotations (XLS 23 kb)
Distance to nearest TAD boundaries (XLS 103 kb)
CRS sequence and expression data for Library 1 (XLS 220 kb)
Composition of Library 2 (XLS 23 kb)
Expression data for Library 2 (XLS 107 kb)
Primers used in this study (XLS 53 kb)
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Maricque, B., Chaudhari, H. & Cohen, B. A massively parallel reporter assay dissects the influence of chromatin structure on cis-regulatory activity. Nat Biotechnol 37, 90–95 (2019). https://doi.org/10.1038/nbt.4285
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