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International Journal of Zoology and Animal Biology Research Article 22 min read

Recently Emerged Genome Wide Computational Enhancer Target Prediction Tools: A Brief Survey

Lim LWK*
* Corresponding author
ISSN: 2639-216X  10.23880/izab-16000440  Received: January 04, 2023  Published: January 31, 2023
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Keywords
Enhancer Target Prediction Supervised Learning Unsupervised Learning Semi-Supervised Learning
Abstract

Enhancers are non-coding genomic regulatory elements capable of elevating gene transcription in various biological as well as developmental stages in the host organism. Discovered since 1981, the enhancers play major roles in genetic disease onset and development, orchestrating gene regulation patterns even across the same species via the sequence variations. To date, predicting enhancers and their targets remain a daunting task as universal enhancer markers are yet to be discovered. Computational enhancer target prediction involves three major approaches: supervised, unsupervised and semi-supervised machine learning methods which work on enhancer target features such as enhancer-promoter distance, closest promoter, co-conservation and correlation of molecular signals. In this review, we introduced some recently emerged enhancer target prediction tools as well as their modus operandi, in hope that we can provide future directions towards the development of a more robust tool to aid in the advancement of enhancer targeted treatment researches.

Introduction

The central dogma forms the foundation of molecular biology and it is widely deemed as the source of all life form on earth [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. The non-coding regions within the genome are the ones responsible for various biological and developmental processes, especially the major regulatory elements such as the promoters and enhancers [11]. Promoters are comparably easier to be identified from the gene it regulates in the genome, with length ranging within a few kilo basepair (kb), located in proximity with the transcription start site (TSS) of coding or non-coding genes. As for enhancers, it is a different story on the other side of the spectrum. Enhancers are cis-regulatory elements that can initiate gene transcription elevation, even from a distance and regardless of orientation, contributing to various disease-related biological progressions known up to now [12, 13, 14].

The term ‘enhancer’ was first coined by De Villiers, et al. [15] in an attempt to describe a short (72 bp) DNA sequence repeat with the capability of triggering and elevating the gene transcription of β-globin gene of rabbit. Then, Banerji, et al. [16] further identified the SV40 enhancer found to heighten the expression of beta-globin gene in HeLa cell line. Generally, enhancers are short DNA elements (50-1500 bp) that functions as platforms for the binding of transcription factors [12]. These transcription factors then work in tandem to collectively contribute to the increase in gene transcription ultimately. Enhancers are more versatile than promoters in terms of their readability (both forward and backwards) as well as their mode of action coverage (up to 1 Mbp from upstream or downstream of genes they regulate in one-to- many or many-to-many manner) [12]. Several enhancers can even function in the form of enhancer-originating RNAs (eRNAs) in which the enhancer brings together both the RNA polymerase II as well as the general transcription factors for the eRNAs to be transcribed [17, 18].

Thus far, genome-wide enhancer target prediction remains a big predicament in the field of genomics as there is no universal enhancer feature identified, to add on to that, their numerous cell and tissue specificities as well as the lack of enhancer primate model species beside human as reference [14, 19, 20, 21]. The enhancer target prediction involves fewer types when compared to genome-wide enhancer prediction [22]. While the genome-wide enhancer prediction involves features like sequences, epigenomic modifications and eRNAs, the enhancer target prediction encompasses features such as enhancer-promoter distance, closest promoter, co-conservation and correlation of molecular signals utilizing supervised, semi-supervised as well as unsupervised machine learning approaches. Studying the whole genome gives us a complete view of all valuable information available for the subject species [11, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36]. In this review, we described in brief the recently emerged genome wide enhancer target prediction tools and further compared their prediction methods. Towards the end of this review, we provided some insights on improvements and development of a more robust enhancer target prediction tool in future.

Gene Regulation by Enhancer

The expression of gene in a given cell or tissue context at a specific spatial and temporal manner is orchestrated by a group of DNA elements named the regulatory components at various biological developmental life stages [37, 38]. The gene expressions are usually regulated via a very strict modus operandi, at every cellular component the gene product passes to eventually become protein through processes like chromatin remodeling, transcription activation, modifications of transcripts, mRNA degradation, translation, posttranslational modifications as well as protein transport and degradation. This is to ensure the host organism can equipped itself with the required gene products to survive and strive in harsh environments. The gene regulation in eukaryotic organisms are much more complicated than that of the prokaryotes as multi-layered networks and cross- acting regulatory elements are actively involved [39].

Gene regulations in eukaryotes are mostly governed by regulatory elements such as the promoter and enhancers via transcription factor binding. The core promoter can be found in all eukaryotic genes and the TATA box (TATAAAAAA) is one of the most commonly discovered examples [39]. Generally, the core promoter is highly conserved across all protein-coding genes in terms of its structure and binding factor as compared to other upstream promoters [40]. One major feature that the enhancers differ from promoter is that the enhancers are located in the non-coding regions and can be either functionally or sequence-conserved across diverse species. Transcription activation requires the RNA Polymerase II to be recruited to the transcription start site following the transcription initiation signals emitted via the interactions between enhancers and promoter as well as the general transcription factors (TFIIA, -B, -D, -E, -F and -H) and chromatin remodeling complexes (ACF, PBAF, SWI/SNF and RSF) [39]. Back in the year 1981, De Villiers, et al. [15] first define the term ‘enhancer’ as DNA elements that can significantly elevate the beta-globin gene expression in rabbit. And thus, the first proposed action of the enhancer is the element that could modify the superhelical density of DNA, improve the accessibility of RNA Polymerase II as well as tolerate nuclear matrix binding [15]. Then, Banerji, et al. [16] further identified the SV40 enhancer that are capable of expression elevation of beta-globin gene in HeLa cell line. They cloned the hemoglobin beta I gene harvested from rabbit and further inserted it into a recombinant expression plasmid with pre- inserted SV40 enhancer, as a result, the expression was found to be 200-fold more than that of the negative control. It is from this study also, Banerji, et al. [16] discovered that this SV40 enhancer can work in both orientations and at any distances from the beta-globin gene. Since then, the enhancer discovery in the human genome progresses exponentially with the discovery of enhancers like the sensory vibrissae enhancer, penile spine enhancer, HACNS1 and forebrain subventricular zone enhancer as well as establishment of various enhancer database like FANTOM5 and VISTA [41, 42, 43].

Besides, enhancers can also recruit transcription factors and serve as binding dock for transcription activation to take place and their sizes between 50 and 1500 base pair generally [12]. They are mostly cis-acting but can sometimes be trans- acting, with the ability to modulate genes as far as 1 Mbp away regardless of upstream or downstream. Enhancers can also work in the form of enhancer-originating RNAs (eRNAs) where eRNAs can enhance the efficiency of enhancers [17, 18]. In this case, RNA Polymerase II is recruited solely by enhancer itself and in turn work in tandem with general transcription factors to transcribe the eRNAs. The vital roles of enhancers in shaping phenotypes evolutionarily as well as being pivotal in essential biological developmental processes such as anatomy progress and morphogenesis are deemed indispensable in proper cell and tissue functioning. It was not until recently that, the various links between enhancer and genetic diseases emerged through experimental discoveries [41, 44], thus created pressing needs for rapid enhancer target identification, especially computationally.

Supervised Machine Learning

The supervised machine learning involves machine learning that is built with strong reliance on high-confidence negative and positively labelled datasets (known enhancer target and non-enhancer target set). This training model is aimed towards the maximum differentiation across the cases from the control sets [45].

One of the supervised machine learning tools for enhancer target predictions that utilizes chromosome conformation data in defining known experimental determined enhancer targets is the IM-PET [46]. IM-PET employs Random Forest classifier to train on ChIA-PET connected enhancer-promoter pairs in both MCF7 as well as K562 cells as positive example datasets. The negative sample dataset used covers the random enhancer-promoter pairs with distances that obeys the background distribution of non-associating genomic loci in a chromatin fiber [47, 48, 49]. The four features employed in this enhancer target prediction which includes correlation of enhancer-promoter activity, enhancer-promoter genomic distance, co-conservation of enhancer and promoter sequences as well as transcription factor expression levels that bind to the enhancer. This tool had achieved 94% AUC, which outperformed PresTIGE, nearest-promoter as well as methods utilized by Ernst, et al. [50] and Thurman, et al. [51].

Another sequence feature based tool utilizing supervised machine learning to uncover enhancer targets is the PETModule which is motif module based [50]. The PETModule sourced from P300 ChIP-seq peaks from seven human cell lines, namely HepG2, SK-N-SH, MCF7. H1-hESC, GM12878, K562 and HeLa-S3, as well as known active enhancers from IMR90 cell line. The positive training set was defined by the random selection from a gigantic pool consisting of 1000 ChIA-PET enhancer-promoter pairs in K562, 1000 Hi-C enhancer-promoter pairs in IMR90 as well as 500 ChIA-PET enhancer-promoter pairs in MCF7 [51, 52]. The negative training datasets were randomly selected genes within 2 Mb around the enhancers. A fair comparison of PETModule across other previously available tools revealed that this tool had outpaced its predecessors (IM-PET and PresTIGE) in terms of AUC (94.9%), precision (0.205) and F1 score (0.286) across all datasets trained.

The CISMAPPER is one powerful tool that can employs the correlation between gene expression and histone mark of transcription factor binding sites across diverse tissue types to predict regulatory targets of a transcription factor, a state-of-art tool that differs from other tools which uses genomic distance [53]. This tool was designed to generate a total of four ranked lists of predictions based on set of scored (TSS, peak) links: two on genes and TSSs as latent targets of the ChIP-ed TF whereas two on TF ChIP-seq peaks as latent regulators of genes and TSSs [53]. The link score was applied to sort the data entries in ascending order in each group respectively. When compared with other tools that predicts based on genomic distances, the CISMAPPER excelled in terms of TSS target predictions and accuracies across six out of the eight diverse tissues: NHEK, HepG2, HeLa-S3, HUVEC, Ag04450, H1-Hesc, K562 and GM12878.

Unsupervised Machine Learning

The unsupervised machine learning is most feasible for the unearthing of hidden and unprecedented configurations straightforwardly from the data. No known and validated information or datasets was required as input for this type of training, thus diminishing the occurrence of false positives in the output but also narrowing the enhancer target output concurrently due to the scarcity in terms of known enhancer target markers.

Ernst, et al. [50] employed the histone modification profile (namely H3K27ac, H3K4me1 and H3K4me2) correlation across enhancer-promoter pair within 125-kbp range to predict enhancer targets from the nine human cell types: GM12878, HUVEC, HMEC, HSMM, NHEK, H1 ES, NHLF, K562 and HepG2. First, the HMM was trained using chromatin states with 10 data tracks for each cell types. Next, a 15 state model was placed in focus to improve the resolution of biologically-meaningful patterns that has reproducibility across different cell types when independently processed. Locations associated to strong promoter state 1 (or strong enhancer state 4) in at least one cell type was subjected to multi-cell type clustering using the k-means algorithm. Utilizing mark intensity-expression correlation data, the logistic regression classifiers were trained to discriminate control pairs from real instances of couples of gene expression data and enhancer states, based on expression data that are arbitrarily re-assigned to dissimilar genes. This approach had achieved a satisfactory result of 67% area under curve (AUC).

The most direct method of predicting enhancer targets is the closest promoter approach applied by Andersson, et al. [45]. They utilized the bidirectional capped RNAs (as measured by CAGE) as predictor of cell enhancer activities.

This method is simple in nature but imperfect in terms of the prediction coverage as the population of enhancers that regulates the nearest promoter covers only 40% of the total genomic enhancer population and multiple promoters can be regulated by one enhancer alone. One way to improve the predictions is to expand the distance range of enhancer target predictions to extend the population of nearest promoter prediction dataset pool [43].

PreSTIGE is another enhancer target prediction tool that pairs the H3K4me1 signals that are cell type-specific with significantly expressed cell type-specific genes to perform its enhancer-promoter pair predictions across 13 different cell types [54]. The multiple linear domain models were utilized to associate cell type-specific enhancers to their target genes. After several evaluation, their finalized domain model were trained to produce the maximum number of predictions, and at the same time, keeping the FDR at its lowest, maintaining the distance boundary at 100 kb as well as considering subset of CTCF sites. For PreSTIGE to function as an enhancer target predictor in a desired cell line, the normalized H3K4me1- enhancer signal must be greater than the background (>10) with the addition circumstances that both the enhancer and the gene must be cell line specific. This approach was found to have identified more enriched enhancer-gene interactions as compared to experimental methods such as ChIA-PET , 3C [55], 5C [56] and eQTL [57, 58, 59, 60, 61].

The DNase I hypersensitive site (DHS) correlation among all candidate pairs situated inside the range of 500- kbp gap was applied by Thurman, et al. [51] to accurately predict enhancer-promoter pairs from the genome. Firstly, emulating the protocols from Farazi, et al. [62] and John, et al. [63], the DNaseI hypersensitivity mapping was completed on 125 cell-types [49]. Then, the datasets were sequenced and mapped, allowing maximum two mismatches. Only the sequence reads that map uniquely to the genome and the data were sorted using the algorithm by Boyle, et al. [64] to achieve DNaseI hypersensitive sites localization. In this study, they had successfully discovered 578,905 DHSs with high correlations (R>0.7) to at least one promoter from the 1,454,901 distal (>2.5kb from transcription start site) DHSs pool from 79 diverse types of cells, with the impressive outcome of extensive map of candidate enhancers with the specific genes they regulate.

Semi-Supervised Machine Learning (Co- Training)

The semi-supervised machine learning (or co-training) encompasses both labelled and unlabeled datasets in its algorithm. This type of machine learning is a powerful approach to enlarge the labelled dataset via the inclusion of its own accurate predictions and therefore explains for is suitability in times when labelled dataset is lacking [63].

The McEnhancer is the one tool that utilizes semi- supervised machine learning to predict enhancer targets together with the other co-regulated genes using third-order interpolated Markov chain model (IMM) via expectation maximization (EM) algorithm in Drosophila melanogaster [64]. First, the McEnhancer was trained on common sequences from labelled (known) DHS-gene pairs before predicting, at one expression at a time, the unlabeled DHSs with analogous subsequences. Then, The McEnhancer- determined enhancer sets were used to train the sparse k-mer-based logistic regression classifiers for expression patterns predictions. One big advantage McEnhancer have over other tools is that it is able to assign multiple target genes to a single enhancer which in turn had improved the resolution of expression classification in a spatial and temporal manner [65, 66].

The Future of Enhancer Target Prediction Tool

The current progress in the field of enhancer target predictions had evolved a big step since the discovery of the first enhancer back in the year 1981. Although the available tools are scarce in amount to date, these tools presented in this paper have provided insights as essential milestone towards the complete mapping of enhancers and its targets within the gnome in the future. The CISMAPPER had proven that its one-of-its-kind approach had achieved greater accuracies as compared to the commonly used feature (genomic distance). Moreover, the McEnhancer had achieved yet another breakthrough with its prediction powers extending towards one-to-many enhancer-promoter pairs prediction, and further provide greater resolution for huge in vivo regulatory datasets that does not have complete alignment with one another. Hariprakash, et al. [67] have listed all the computational biology solutions pertaining to enhancers-target gene pairs identification in their mini review. Moore, et al. [68] further described a curated benchmark of enhancer-gene interactions with regards to enhancer-target gene prediction methods. These guidelines and benchmark will greatly aid in curating all enhancer-gene outcomes for future comparisons and improvements. As the outcome of these research were to ultimately be being able to accurately predict enhancer targets in any cell context, the enhancer target prediction tools will have to improve in terms of their robustness where multiple enhancer target features are included for predictions. For instance, the PETModule can be further improved with the addition of features such as CTCF locations. Besides, the dynamic regulation of enhancers under specific conditions is also deemed essential to make this tool more robust in terms of wider application spectrum [69, 70, 71, 72, 73]. All in all, it is both as important to include more enhancer target features into the prediction and also to conduct experimental validations following computational predictions.

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  72. Lim LWK, Chung HH, Chong YL, Lee NK (2018a) A survey of recently emerged computational enhancer predictor tools. Computational Biology and Chemistry 74(1): 132-
  73. Visel A, Akiyama JA, Shoukry M, Afzal V, Rubin EM, et al. (2009) Functional autonomy of distant-acting human enhancers. Genomics 93(6): 509-513.
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@article{lim2023,
  title   = {Recently Emerged Genome Wide Computational Enhancer Target
Prediction Tools: A Brief Survey},
  author  = {Lim LWK},
  journal = {International Journal of Zoology and Animal Biology},
  year    = {2023},
  volume  = {6},
  number  = {1},
  doi     = {10.23880/izab-16000440}
}
Lim LWK (2023). Recently Emerged Genome Wide Computational Enhancer Target
Prediction Tools: A Brief Survey. International Journal of Zoology and Animal Biology, 6(1). https://doi.org/10.23880/izab-16000440
TY  - JOUR
TI  - Recently Emerged Genome Wide Computational Enhancer Target
Prediction Tools: A Brief Survey
AU  - Lim LWK
JO  - International Journal of Zoology and Animal Biology
PY  - 2023
VL  - 6
IS  - 1
DO  - 10.23880/izab-16000440
ER  -