Identification and dynamic quantification of regulatory elements using total RNA

Abstract

The spatial and temporal regulation of transcription initiation is pivotal for controlling gene expression. Here, we introduce capped-small RNA-seq (csRNA-seq), which uses total RNA as starting material to detect transcription start sites (TSSs) of both stable and unstable RNAs at single-nucleotide resolution. csRNA-seq is highly sensitive to acute changes in transcription and identifies an order of magnitude more regulated transcripts than does RNA-seq. Interrogating tissues from species across the eukaryotic kingdoms identified unstable transcripts resembling enhancer RNAs, pri-miRNAs, antisense transcripts, and promoter upstream transcripts in multicellular animals, plants, and fungi spanning 1.6 billion years of evolution. Integration of epigenomic data from these organisms revealed that histone H3 trimethylation (H3K4me3) was largely confined to TSSs of stable transcripts, whereas H3K27ac marked nucleosomes downstream from all active TSSs, suggesting an ancient role for posttranslational histone modifications in transcription. Our findings show that total RNA is sufficient to identify transcribed regulatory elements and capture the dynamics of initiated stable and unstable transcripts at singlenucleotide resolution in eukaryotes.

Mechanisms and consequences of subcellularRNAlocalization across diverse cell types

“…Identification of potential cis ‐acting elements using computational methods (eg, MEME, 99 HOMER, 100 SeAMotE 101 and others dependent on structure 102–105 ) have had limited success. Despite our increasing understanding of principles governing cis ‐localization elements using specific examples, we still lack a systematic characterization of general rules.…”

MEIRLOP: improving score-based motif enrichment by incorporating sequence bias covariates

“…We started by extracting sequences and scores for differential TSS data previously generated as described in Duttke et al [38]. We then ran MEIRLOP on the scored TSS sequences.…”

“…Differential TSS were found from murine BMDMs as described in Duttke et al [38]. We performed enrichment for motifs found within +/− 150 bp of the TSS, and scored sequences by the differential log 2 fold change between KLA stimulated and unstimulated control conditions as computed by HOMER getDiffExpression.pl (which wraps DESeq2 to calculate differential statistics while accounting for library size and replicates).…”

“…To demonstrate our method's efficacy when analyzing changes in transcription with diverse data types, we applied MEIRLOP to the analysis of capped short (cs)RNA-seq data generated in macrophages activated by TLR4-agonist KLA for 1 h or mock-treated controls. csRNA-seq is an approach that isolates initiating transcripts at both promoters and enhancers to directly assess the transcriptional activity of regulatory elements genome-wide [38]. We analyzed 90,857 transcription start sites (TSS) from csRNA-seq profiling of murine bone-marrow derived macrophages (BMDMs) activated by Kdo2-lipid A (KLA), as previously analyzed in Duttke et al [38].…”

“…csRNA-seq is an approach that isolates initiating transcripts at both promoters and enhancers to directly assess the transcriptional activity of regulatory elements genome-wide [38]. We analyzed 90,857 transcription start sites (TSS) from csRNA-seq profiling of murine bone-marrow derived macrophages (BMDMs) activated by Kdo2-lipid A (KLA), as previously analyzed in Duttke et al [38]. We searched for motifs within +/− 150 bp of the TSS, and scored them by their log 2 fold change of csRNA-seq signal between KLA stimulation and control.…”