Dataset Information

Repeated mutation of a developmental enhancer contributed to human thermoregulatory evolution.

ABSTRACT: Humans sweat to cool their bodies and have by far the highest eccrine sweat gland density among primates. Humans' high eccrine gland density has long been recognized as a hallmark human evolutionary adaptation, but its genetic basis has been unknown. In humans, expression of the Engrailed 1 (EN1) transcription factor correlates with the onset of eccrine gland formation. In mice, regulation of ectodermal En1 expression is a major determinant of natural variation in eccrine gland density between strains, and increased En1 expression promotes the specification of more eccrine glands. Here, we show that regulation of EN1 has evolved specifically on the human lineage to promote eccrine gland formation. Using comparative genomics and validation of ectodermal enhancer activity in mice, we identified a human EN1 skin enhancer, hECE18. We showed that multiple epistatically interacting derived substitutions in the human ECE18 enhancer increased its activity compared with nonhuman ape orthologs in cultured keratinocytes. Repression of hECE18 in human cultured keratinocytes specifically attenuated EN1 expression, indicating this element positively regulates EN1 in this context. In a humanized enhancer knock-in mouse, hECE18 increased developmental En1 expression in the skin to induce the formation of more eccrine glands. Our study uncovers a genetic basis contributing to the evolution of one of the most singular human adaptations and implicates multiple interacting mutations in a single enhancer as a mechanism for human evolutionary change.

SUBMITTER: Aldea D

PROVIDER: S-EPMC8072367 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology.

Project description:The development of the mammalian kidney is a highly complex process dependent upon the interplay of various cell types, secreted morphogens, and the extra-cellular matrix (ECM). Although integrins are the most important receptors for ECM proteins and are ubiquitously expressed during kidney development, mice lacking expression of integrin ?3 (Itga3) do not demonstrate a reduced number of nephrons, but mostly a disorganized GBM (glomerular basement membrane) leading to proteinuria. Thus, ITGA3 is considered mostly a passive GBM stabilizer and not an active player in nephrogenesis. Recently, mutations in the human ITGA3 were shown to cause congenital nephrotic syndrome, epidermolysis bullosa and interstitial lung disease, otherwise termed NEP syndrome (Nephrotic syndrome, Epidermolysis bullosa and Pulmonary disease). Herein, we performed histological and molecular analysis on the kidneys of a single patient from the initial cohort harboring an ITGA3 mutation, to illuminate the role of ITGA3 in human renal development. We show the patient to harbor a unique phenotype at birth, including severe unilateral renal hypodysplasia. Interrogation of global gene expression in the hypodysplastic kidney versus three controls (fetal, child and adult kidneys) revealed perturbed expression in several renal developmental pathways implicated in hypodysplasia, including the Wnt, BMP (bone morphogenetic protein) and TGF (transforming growth factor) pathways. Moreover, the affected kidney showed upregulation of early embryonic genes (e.g. OCT4 and PAX8) concomitant with downregulated kidney differentiation markers, implying a defect in proper renal differentiation. In conclusion, we show for the first time that ITGA3 is not merely a passive anchor for renal ECM proteins, as predicted by mouse models. Instead, our results may suggest it plays a central role in the interplay of cells, morphogens and ECM, required for proper nephrogenesis, thus adding ITGA3 to the list of CAKUT (congenital anomalies of the kidney and urinary tract)-causing genes.

Dataset Information

Repeated mutation of a developmental enhancer contributed to human thermoregulatory evolution.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets