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Enhancer jungles establish robust tissue-specific regulatory control in the human genome.

ABSTRACT: An increasing number of studies suggest that functionally redundant enhancers safeguard development via buffering gene expression against environmental and genetic perturbations. Here, we identified over-represented clusters of enhancers (enhancer jungles or EJs) in human B lymphoblastoid cells. We found that EJs tend to associate with genes involved in the activation of the immune system response. Although spanning multiple genes, the enhancers within an EJ tend to collaborate with each other on regulating a single gene. The employment of homotypic transcription factor binding sites (TFBSs) in EJ enhancers and heterotypic TFBSs between constituent enhancers within an EJ may safeguard a robust transcriptional output of the target gene. EJ enhancers evolve under a weaker selective pressure compared to regular enhancers (REs), and approximately 35% of EJs do not have orthologues in the mouse genome. In GM12878, these human-specific EJs appear to regulate genes associated with the adaptive immune system response, while the conserved EJs are associated with innate immunity. Recently acquired human EJs are associated with the higher level of target gene expression compared with conserved EJs, thus facilitating the environmental adaptation of the organism during evolution. In short, the existence of EJs is a common regulatory architecture conferring a robust regulatory control for key lineage genes.

SUBMITTER: Li S

PROVIDER: S-EPMC7082202 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Researchers seeking to unlock the genetic basis of human physiology and diseases have been studying gene transcription regulation. The temporal and spatial patterns of gene expression are controlled by mainly non-coding elements known as cis-regulatory modules (CRMs) and epigenetic factors. CRMs modulating related genes share the regulatory signature which consists of transcription factor (TF) binding sites (TFBSs). Identifying such CRMs is a challenging problem due to the prohibitive number of sequence sets that need to be analyzed.We formulated the challenge as a supervised classification problem even though experimentally validated CRMs were not required. Our efforts resulted in a software system named CrmMiner. The system mines for CRMs in the vicinity of related genes. CrmMiner requires two sets of sequences: a mixed set and a control set. Sequences in the vicinity of the related genes comprise the mixed set, whereas the control set includes random genomic sequences. CrmMiner assumes that a large percentage of the mixed set is made of background sequences that do not include CRMs. The system identifies pairs of closely located motifs representing vertebrate TFBSs that are enriched in the training mixed set consisting of 50% of the gene loci. In addition, CrmMiner selects a group of the enriched pairs to represent the tissue-specific regulatory signature. The mixed and the control sets are searched for candidate sequences that include any of the selected pairs. Next, an optimal Bayesian classifier is used to distinguish candidates found in the mixed set from their control counterparts. Our study proposes 62 tissue-specific regulatory signatures and putative CRMs for different human tissues and cell types. These signatures consist of assortments of ubiquitously expressed TFs and tissue-specific TFs. Under controlled settings, CrmMiner identified known CRMs in noisy sets up to 1:25 signal-to-noise ratio. CrmMiner was 21-75% more precise than a related CRM predictor. The sensitivity of the system to locate known human heart enhancers reached up to 83%. CrmMiner precision reached 82% while mining for CRMs specific to the human CD4+ T cells. On several data sets, the system achieved 99% specificity.These results suggest that CrmMiner predictions are accurate and likely to be tissue-specific CRMs. We expect that the predicted tissue-specific CRMs and the regulatory signatures broaden our knowledge of gene transcription regulation.

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Enhancer jungles establish robust tissue-specific regulatory control in the human genome.

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