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Genome-wide association studies (GWAS) and expression analyses implicate noncoding regulatory regions as harboring risk factors for psychiatric disease, but functional characterization of these regions remains limited. We performed capture STARR-sequencing of over 78,000 candidate regions to identify active enhancers in primary human neural progenitor cells (phNPCs). We selected candidate regions by integrating data from NPCs, prefrontal cortex, developmental timepoints, and GWAS. Over 8,000 regions demonstrated enhancer activity in the phNPCs, and we linked these regions to over 2,200 predicted target genes. These genes are involved in neuronal and psychiatric disease-associated pathways, including dopaminergic synapse, axon guidance, and schizophrenia. We functionally validated a subset of these enhancers using mutation STARR-sequencing and CRISPR deletions, demonstrating the effects of genetic variation on enhancer activity and enhancer deletion on gene expression. Overall, we identified thousands of highly active enhancers and functionally validated a subset of these enhancers, improving our understanding of regulatory networks underlying brain function and disease., Competing Interests: Competing interests: Kevin P. White is a shareholder of Tempus Labs, Inc. and Provaxus, Inc. All other authors declare that they have no competing interests.

The blueprints to development, response to the environment, and cellular function are largely the manifestation of distinct gene expression programs controlled by the spatiotemporal activity of cis -regulatory elements. Although biochemical methods for identifying accessible chromatin - a hallmark of active cis -regulatory elements - have been developed, approaches capable of measuring and quantifying cis -regulatory activity are only beginning to be realized. Massively Parallel Reporter Assays coupled to chromatin accessibility profiling present a high-throughput solution for testing the transcription-activating capacity of millions of putatively regulatory DNA sequences in parallel. However, clear computational pipelines for analyzing these high-throughput sequencing-based reporter assays are lacking. In this protocol, I layout and rationalize a computational framework for the processing and analysis of Assay for Transposase Accessible Chromatin profiling followed by Self-Transcribed Active Regulatory Region sequencing (ATAC-STARR-seq) data from a recent study in Zea mays. The approach described herein can be adapted to other sequencing-based reporter assays and is largely agnostic to the model organism with the appropriate input substitutions., Competing Interests: Competing interests A.P.M. declares no competing interests.

High-throughput methods such as RNA-seq, ChIP-seq, and ATAC-seq have well-established guidelines, commercial kits, and analysis pipelines that enable consistency and wider adoption for understanding genome function and regulation. STARR-seq, a popular assay for directly quantifying the activities of thousands of enhancer sequences simultaneously, has seen limited standardization across studies. The assay is long, with more than 250 steps, and frequent customization of the protocol and variations in bioinformatics methods raise concerns for reproducibility of STARR-seq studies. Here, we assess each step of the protocol and analysis pipelines from published sources and in-house assays, and identify critical steps and quality control (QC) checkpoints necessary for reproducibility of the assay. We also provide guidelines for experimental design, protocol scaling, customization, and analysis pipelines for better adoption of the assay. These resources will allow better optimization of STARR-seq for specific research needs, enable comparisons and integration across studies, and improve the reproducibility of results.

High-throughput reporter assays such as self-transcribing active regulatory region sequencing (STARR-seq) have made it possible to measure regulatory element activity across the entire human genome at once. The resulting data, however, present substantial analytical challenges. Here, we identify technical biases that explain most of the variance in STARR-seq data. We then develop a statistical model to correct those biases and to improve detection of regulatory elements. This approach substantially improves precision and recall over current methods, improves detection of both activating and repressive regulatory elements, and controls for false discoveries despite strong local correlations in signal.

Genetic engineering of cis-regulatory elements in crop plants is a promising strategy to ensure food security. However, such engineering is currently hindered by our limited knowledge of plant cis-regulatory elements. Here, we adapted STARR-seq — a technology for the high-throughput identification of enhancers — for its use in transiently transformed tobacco leaves. We demonstrate that the optimal placement in the reporter construct of enhancer sequences from a plant virus, pea and wheat was just upstream of a minimal promoter, and that none of these four known enhancers was active in the 3′-UTR of the reporter gene. The optimized assay sensitively identified small DNA regions containing each of the four enhancers, including two whose activity was stimulated by light. Furthermore, we coupled the assay to saturation mutagenesis to pinpoint functional regions within an enhancer, which we recombined to create synthetic enhancers. Our results describe an approach to define enhancer properties that can be performed in potentially any plant species or tissue transformable by Agrobacterium and that can use regulatory DNA derived from any plant genome.One-sentence summaryWe developed a high-throughput assay in transiently transformed tobacco leaves that can identify enhancers, characterize their functional elements and detect condition-specific enhancer activity.

Massively parallel reporter assays (MPRAs) test the capacity of putative gene regulatory elements to drive transcription on a genome-wide scale. Most gene regulatory activity occurs within accessible chromatin, and recently described methods have combined assays that capture these regions—such as assay for transposase-accessible chromatin using sequencing (ATAC-seq)—with self-transcribing active regulatory region sequencing (STARR-seq) to selectively assay the regulatory potential of accessible DNA (ATAC-STARR-seq). Here, we report an integrated approach that quantifies activating and silencing regulatory activity, chromatin accessibility, and transcription factor (TF) occupancy with one assay using ATAC-STARR-seq. Our strategy, including important updates to the ATAC-STARR-seq assay and workflow, enabled high-resolution testing of ∼50 million unique DNA fragments tiling ∼101,000 accessible chromatin regions in human lymphoblastoid cells. We discovered that 30% of all accessible regions contain an activator, a silencer, or both. Although few MPRA studies have explored silencing activity, we demonstrate that silencers occur at similar frequencies to activators, and they represent a distinct functional group enriched for unique TF motifs and repressive histone modifications. We further show that Tn5 cut-site frequencies are retained in the ATAC-STARR plasmid library compared to standard ATAC-seq, enabling TF occupancy to be ascertained from ATAC-STARR data. With this approach, we found that activators and silencers cluster by distinct TF footprint combinations, and these groups of activity represent different gene regulatory networks of immune cell function. Altogether, these data highlight the multilayered capabilities of ATAC-STARR-seq to comprehensively investigate the regulatory landscape of the human genome all from a single DNA fragment source.

STARR-seq and its variants have been widely used to characterize enhancers. However, it has been reported that up to 87% of STARR peaks are located in repressive chromatins and are not functional in the tested cells. While some of the STARR peaks in repressive chromatins might be active in other cell/tissue types, some others might be false positives. Meanwhile, many active enhancers may not be identified by the current STARR-seq methods. However, the prevalence of and underlying causes for the artifacts are not fully understood. Based on predictedcis-regulatory modules (CRMs) and non-CRMs in the human genome as well as predicted active CRMs and non-active CRMs in a few human cell lines with STARR-seq data available, we reveal prevalent false positives and false negatives in STARR peaks and possible underlying causes. Our results will help design strategies to improve STARR-seq methods and interpret the results.

Enhancers are short regions of non-coding DNA that increase transcription rates of genes despite being located distantly from the genes themselves [5]. Enhancers are identified through experimental techniques such as ChIP-Seq or CUT&RUN with H3K4me1 and H3K27ac histone modifications, self-transcribing active regulatory region sequencing (STARR-Seq), and massively parallel reporter assays (MPRA). Machine learning models have been used in conjunction with experimental data to identify enhancer activity from sequences [3], predict enhancer-transcription factor interactions [4], and decode the enhancer regulatory language [2]. We describe a framework that connects peak calling errors to the prediction accuracy of sequence models. The key assumptions of our framework are that (1) enhancers have consistent sequence patterns that can be used to separate enhancers from control sequences, (2) errors in the training data impact prediction accuracies in predictable ways, and (3) prediction accuracy is a useful proxy for evaluating peak calling accuracy. In the framework, data sets are constructed from peak (positive) and randomly sampled (control) sequences. Machine learning models are trained and evaluated on the sequences in a cross-chromosome (cross-fold) setup. Lastly, precision of the originating peaks are evaluated by calculating true and false positive rates. We applied our framework to evaluate peaks for D. melanogaster STARR-Seq data [1] called with the MACS software [6]. Although designed for ChIP-Seq data, MACS can be used to process other types of data, but users must be careful about parameter choices. We evaluated different parameter combinations with our framework and visual comparisons of called peaks. True and false positive rates ranged from a high of 88.0% to a low of 74.7% and from a low of 18.6% to a high of 49.4%, respectively. The default MACS parameters produced the highest true and lowest false positive rates, suggesting that the default parameters are also suitable for STARR-Seq data. Our results demonstrate the utility of our framework through a practical application and provide a base for future development.

Embryonic stem cells (ESCs) can differentiate into any given cell type and therefore represent a versatile model to study the link between gene regulation and differentiation. To quantitatively assess the dynamics of enhancer activity during the early stages of murine ESC differentiation, we analyzed accessible genomic regions using STARR-seq, a massively parallel reporter assay. This resulted in a genome-wide quantitative map of active mESC enhancers, in pluripotency and during the early stages of differentiation. We find that only a minority of accessible regions is active and that such regions are enriched near promoters, characterized by specific chromatin marks, enriched for distinct sequence motifs, and modeling shows that active regions can be predicted from sequence alone. Regions that change their activity upon retinoic acid-induced differentiation are more prevalent at distal intergenic regions when compared to constitutively active enhancers. Further, analysis of differentially active enhancers verified the contribution of individual TF motifs toward activity and inducibility as well as their role in regulating endogenous genes. Notably, the activity of retinoic acid receptor alpha (RARα) occupied regions can either increase or decrease upon the addition of its ligand, retinoic acid, with the direction of the change correlating with spacing and orientation of the RARα consensus motif and the co-occurrence of additional sequence motifs. Together, our genome-wide enhancer activity map elucidates features associated with enhancer activity levels, identifies regulatory regions disregarded by computational prediction tools, and provides a resource for future studies into regulatory elements in mESCs.

Enhancers are DNA sequences that can strengthen transcription initiation. However, the global identification of plant enhancers is complicated due to uncertainty in the distance and orientation of enhancers, especially in species with large genomes. In this study, we performed self-transcribing active regulatory region sequencing (STARR-seq) for the first time to identify enhancers across the barley genome. A total of 7323 enhancers were successfully identified, and among 45 randomly selected enhancers, over 75% were effective as validated by a dual-luciferase reporter assay system in the lower epidermis of tobacco leaves. Interestingly, up to 53.5% of the barley enhancers were repetitive sequences, especially transposable elements (TEs), thus reinforcing the vital role of repetitive enhancers in gene expression. Both the common active transcription marker H3K4me3 and repressive histone marker H3K27me3 were abundant among the barley STARR-seq enhancers. In addition, the functional range of barley STARR-seq enhancers seemed much broader than that of rice or maize and extended to ± 100 KB of the gene body, and this finding was consistent with the high expression levels of genes in the genome. This work specifically depicts the unique features of barley enhancers and provides available barley enhancers for further utilization.

The blueprints to development, response to the environment, and cellular function are largely the manifestation of distinct gene expression programs controlled by the spatiotemporal activity ofcis-regulatory elements. Although biochemical methods for identifying accessible chromatin – a hallmark of activecis-regulatory elements – have been developed, approaches capable of measuring and quantifyingcis-regulatory activity are only beginning to be realized. Massively Parallel Reporter Assays coupled to chromatin accessibility profiling present a high-throughput solution for testing the transcription-activating capacity of millions of putatively regulatory DNA sequences in parallel.However, clear computational pipelines for analyzing these high-throughput sequencing-based reporter assays are lacking. In this protocol, I layout and rationalize a computational framework for the processing and analysis of Assay for Transposase Accessible Chromatin profiling followed by Self-Transcribed Active Regulatory Region sequencing (ATAC-STARR-seq) data from a recent study inZea mays. The approach described herein can be adapted to other sequencing-based reporter assays and is largely agnostic to the model organism with the appropriate input substitutions.

STARR-seq assesses millions of fragments in parallel measuring enhancer activity quantitatively. Here we show that STARR-seq is critically flawed with a systematic error in the cells of Arabidopsis thaliana (A. thaliana). Large amount of self-transcripts (STs) is lost during reverse transcription because these STs are polyadenylated after alternative polyadenylation sites (APAS) inside the test sequences. We solved this problem by using specially designed primer and recovered self-transcribed sequences independent from the PAS usage. In A. thaliana, we identified active enhancers and also enhancers quiescent in their endogenous genomic loci. Different from traditional STARR-seq identified enhancers, enhancers identified by new method are highly enriched in sequences proximal to the 5’ and 3’ ends of genes, and their epigenetic states correlate with gene expression levels. Our solution applies to methods based on self-transcript quantification. In addition, our results provide an invaluable functional enhancer activity map and insights into the functional complexity of enhancers in A. thaliana.

Ability to functionally screen gene regulatory sequences, such as promoters and enhancers, in high throughput manner is an important prerequisite for many basic and translational research programs. One of the methods that allow such screening is STARR-seq, or self-transcribing active regulatory region sequencing. It allows to quickly screen millions of candidate sequences in the cell type of interest. However, it does rely on transfection as a delivery method which can be a limiting-step for some hard-to-transfect cells such as senescent cells. Here we show that integration-deficient and integration-competent promoterless lentiviral particles can be used to deliver STARR-seq constructs into cells. These constructs reported CMV enhancer activity both at protein and mRNA level. While further validations are necessary, ability to deliver STARR-seq libraries using lentiviral particles will significantly improve the versatility and usability of such a method.Competing Interest StatementThe authors have declared no competing interest.View Full Text

STARR-Seq is a high-throughput technique for directly identifying genomic regions with enhancer activity [1]. Genomic DNA is sheared, inserted into artificial plasmids designed so that DNA with enhancer activity trigger self-transcription, and transfected into culture cells. The resulting RNA is converted back into cDNA, sequenced, and aligned to a reference genome. "Peaks" are called by comparing observed read depth at each point to an expected read depth from control DNA using a statistical test. Examples of peak calling methods based on read depth include MACS2 [4], basicSTARRSeq, and STARRPeaker [3]. It is challenging to accurately distinguish between real peaks and artifacts in regions where mean read depth is low but the variance is high. Fortunately, enhancer activity is strongly correlated with sequence content. We propose using sequence-based machine learning models in a semi-supervised framework to filter peaks. 501-bp sequences centered on the a11k STARR peaks from [1] were extracted from the Drosophila melanogaster dm3 genome. Randomly-sampled 501-bp sequences were used as a negative set. Peaks were filtered using a Bonferroni-corrected significance value (α = 0.05) to create a "high-confidence" subset of a2.2k peaks. A Logistic Regression model with k-mer count features was trained on the high-confidence peak sequences and their negatives and used to classifying the remaining a8.8k peak sequences. The self-trained, sequenced-based model identified an additional a3.7k candidate enhancers ("medium confidence"). The remaining a5k STARR peaks were considered "low confidence" peaks. We plotted histograms of the read depth log-fold change for the three sets of peaks (high, medium, and low confidence) (see Figure 1). The distributions for the medium- and low-confidence peaks overlapped significantly. The sequence-based model identified enhancer candidates that would otherwise be filtered out using read depth alone. We called peaks for the 4 D. melanogaster FAIRE-Seq data sets from [2]. Sequencing data were cleaned with Trimmomatic, aligned to the dm3 genome with bwa backtrack, and filtered for mapping quality (q