Identifying transcriptional cis‐regulatory modules in animal genomes

Overview

Kushal Suryamohan, Marc S. Halfon

Published Online: Dec 29 2014

DOI: 10.1002/wdev.168

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Gene expression is regulated through the activity of transcription factors (TFs) and chromatin‐modifying proteins acting on specific DNA sequences, referred to as cis‐regulatory elements. These include promoters, located at the transcription initiation sites of genes, and a variety of distal cis‐regulatory modules (CRMs), the most common of which are transcriptional enhancers. Because regulated gene expression is fundamental to cell differentiation and acquisition of new cell fates, identifying, characterizing, and understanding the mechanisms of action of CRMs is critical for understanding development. CRM discovery has historically been challenging, as CRMs can be located far from the genes they regulate, have few readily identifiable sequence characteristics, and for many years were not amenable to high‐throughput discovery methods. However, the recent availability of complete genome sequences and the development of next‐generation sequencing methods have led to an explosion of both computational and empirical methods for CRM discovery in model and nonmodel organisms alike. Experimentally, CRMs can be identified through chromatin immunoprecipitation directed against TFs or histone post‐translational modifications, identification of nucleosome‐depleted ‘open’ chromatin regions, or sequencing‐based high‐throughput functional screening. Computational methods include comparative genomics, clustering of known or predicted TF‐binding sites, and supervised machine‐learning approaches trained on known CRMs. All of these methods have proven effective for CRM discovery, but each has its own considerations and limitations, and each is subject to a greater or lesser number of false‐positive identifications. Experimental confirmation of predictions is essential, although shortcomings in current methods suggest that additional means of validation need to be developed. WIREs Dev Biol 2015, 4:59–84. doi: 10.1002/wdev.168 This article is categorized under: Gene Expression and Transcriptional Hierarchies > Regulatory Mechanisms Gene Expression and Transcriptional Hierarchies > Gene Networks and Genomics Technologies > Analysis of the Transcriptome

cis‐Regulatory modules (CRMs). (a) Modular nature of CRMs. The region downstream of the Drosophila even skipped (eve) gene has numerous CRMs (pink boxes), each of which controls a different portion of the gene's expression pattern. Reporter gene expression directed by individual CRMs (black) is shown superimposed on Eve protein expression (brown). During the early blastoderm stages, individual stripes are regulated by separate CRMs (S1, S4‐6, S5), as is later embryonic expression in the somatic musculature (M). Expression from other CRMs including those in the 5′ flanking region are not pictured. Photos courtesy of James Jaynes and Miki Fujioka. (b) Generalized mechanisms of CRM function. Active CRMs (orange), bound by multiple transcription factors (TFs), contact their associated promoter by DNA looping. Either through direct contact or via bridging interactions from coactivators, the CRMs help to recruit and/or stabilize RNA polymerase II and the general transcription factors (GTFs). TSS, transcription start site.

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Supervised motif‐blind cis‐regulatory module (CRM) discovery. (a) A set of CRMs with related activity (e.g., midbrain, heart, wing, and muscle) is selected as a training set, and a set of similarly sized non‐CRMs as a background (BKG) set. The training set can also include orthologous sequences from related species. (b) The k‐mer profile of the sequence sets is obtained and used to train one of several statistical models. (c) The score for a given sequence S is the log‐likelihood ratio of the models for the positive (training) and negative (background) sets on S. (d) Overlapping sequence windows are scored throughout the genome. High‐scoring windows (stars) are predicted CRMs.

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Transcription factor‐binding site (TFBS) conservation in aligned versus alignment‐free settings. Each colored polygon represents a binding site. (a) When considering conservation based on sequence alignment only a fraction of the binding sites are seen to be conserved [4/8 for cis‐regulatory module (CRM) A, 4/7 for CRM B], and several different alignments can be proposed. Arrows represent aligned sites, with gray arrows indicating alternative alignments. Note that choosing the proper alignment is significant, as the identities of the conserved sites are sensitive to the chosen alignment; in this example, presence of the sites represented by the purple oval and the red octagon depends on alignment choice. (b) In an alignment‐free setting, TFBSs are identified and considered conserved if they appear in both sequences, regardless of how they are ordered. Using this approach, seven of eight sites from CRM A and all seven sites from CRM B are conserved. Moreover, the full complement of different sites is conserved, with merely a small reduction in the number of sites represented by the red octagon. The same principle applies to nucleotide‐based (rather than motif‐based) alignments, where subsequence (k‐mer) composition can be substituted for motifs (see text).

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Computational approaches to cis‐regulatory module (CRM) discovery. Computational methods for CRM discovery fall into three basic classes. (a) Comparative genomics methods find regions of conservation between two or more species, either by sequence alignment (‘aligned sequence’, shown here as a PhastCons score over multiple species) or by alignment of transcription factor‐binding site (TFBS) motifs (aligned motifs). A horizontal bar indicates predicted CRMs. Note that a method based on alignment of motifs may miss important unaligned compensatory sites (arrows). (b) Motif‐based methods identify clusters of TFBS motifs, usually with some foreknowledge of which TFBSs are expected for the CRMs being sought (the ‘transcriptional code’). Here, a tight cluster of multiple red octagonal, blue square, and green triangle motifs predicts the CRM (horizontal bar). (c) Motif‐blind methods rely on statistical models of the DNA sequence rather than identification of motifs. Regions of the genome that receive high scores based on a particular model are predicted as CRMs (green box).

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Reporter genes. The ‘gold‐standard’ test for cis‐regulatory module (CRM) function is the reporter gene assay, in which a putative CRM sequence is cloned upstream of a minimal promoter‐reporter cassette sequence that on its own has little or no transcription. The reporter gene can be any gene whose expression is easily assayed. Current common reporters include luciferase, β‐galactosidase (the Escherichia coli lacZ gene), and fluorescent proteins such as the Aequorea victoria green fluorescent protein (GFP) and its derivatives. lacZ and the fluorescent protein genes are particularly suitable for use as in vivo reporters as they are readily assayed in whole animals or histological sections, whereas luciferase provides high sensitivity in cell culture assays. The recent availability of affordable next‐generation sequencing has enabled the development of methods using DNA barcodes or even the CRM sequence itself as a reporter (see main text). While high‐throughput, these approaches however lose the valuable ability possessed by visible reporter genes to spatially localize domains of CRM activity. Mouse embryo photo courtesy of VISTA Enhancer Browser, cell culture photo courtesy of Satrajit Sinha.

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Experimental methods for cis‐regulatory module (CRM) discovery. (a) Genomic DNA to be tested for CRM function can be isolated in an unbiased way through shearing or digestion (small arrows), or in a more directed way by polymerase chain reaction amplification. The fragments are then tested for regulatory activity through one of several assays (d–f). (b) CRMs can also be predicted through assays for accessible chromatin, in which ‘open’ chromatin regions (small arrows) can be distinguished from regions of less accessible chromatin. (c) An additional method used for CRM discovery is chromatin immunoprecipitation (ChIP)‐seq directed against histone modifications (pink) or one or more TFs (blue). For both chromatin accessibility and ChIP‐seq assays, predicted CRM regions identified by next‐generation sequencing (boxed orange peak in b, c) can be cloned and validated by the assays in panels d–f. (d) Cloned sequences can be tested individually by traditional reporter gene assays in transgenic animals or cells (middle), or in a higher throughput fashion following fluorescence‐activated cell sorting and next‐generation sequencing. (e) Alternatively, reporter constructs can be built to contain unique sequence ‘barcodes’ which can then be matched to the associated CRMs subsequent to RNA‐seq analysis. (f) In STARR‐seq, the CRM serves as its own reporter, allowing for direct identification following RNA‐seq analysis.

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Transcription factor‐binding site (TFBS) motifs. A TFBS motif describes the sequences to which a TF can bind, and can be represented in various ways, each with its own advantages and disadvantages. (a) A subset of sequences to which the Drosophila TF Paired binds in a bacterial one‐hybrid assay, drawn from FlyFactorSurvey. The simplest representation is as a single text string consensus sequence (b). In the consensus sequence, a single base is shown when it occurs in more than half of the binding‐site sequences and at least twice as much as the next most frequently occurring base at that position; otherwise, degenerate symbols are used. The example in (b) has H = {A, C, T} in the first column and Y = {C, T} in the final position. Consensus sequences have the advantage of being simple to portray and easy to search for, but convey limited information about the range of individual sequences comprising the motif. (c) A better sense of nucleotide variability at each position is seen with a motif logo. Logos can be derived from a position frequency matrix (d), which totals the presence of each base at each position and which can also be used to develop position weight matrices (PWMs) such as the logodds‐adjusted matrix in (e). PWMs reflect the probability distributions of the four possible nucleotides at each location and relate closely to the binding energy of TFs to the DNA motifs. PWMs lend themselves well to sophisticated sequence‐search algorithms and are the basis for most bioinformatics approaches to TFBS detection.

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