Modeling uniquely human gene regulatory function in humanized mice

Dutrow, Emily V.; Emera, Deena; Yim, Kristina; Uebbing, Severin; Kocher, Acadia A.; Krenzer, Martina; Nottoli, Timothy; Burkhardt, Daniel; Krishnaswamy, Smita; Louvi, Angeliki; Noonan, James P.

doi:10.1101/2019.12.11.873075

Cited by 5 publications

(2 citation statements)

References 88 publications

(201 reference statements)

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“…This sequence is located in an intron of the gene, and actually lies within a human craniofacial superenhancer region (Figure 5A), suggesting its role in the craniofacial apparatus. Despite its proximity to known craniofacial regulatory regions, HACNS1 has been proposed to regulate Gbx2 in the mouse limb (113). However, this gene is expressed at relatively low levels in human craniofacial tissues (97) and the mouse molar compared to Agap1.…”

Section: Data Integration Prioritizes Dental Enhancers At Predicted O...mentioning

confidence: 99%

Integration of multimodal data in the developing tooth reveals candidate regulatory loci driving human odontogenic phenotypes

Winchester

Cotney

2022

Front. Dent. Med

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Human odontogenic aberrations such as abnormal tooth number and delayed tooth eruption can occur as a symptom of rare syndromes or, more commonly, as nonsyndromic phenotypes. These phenotypes can require extensive and expensive dental treatment, posing a significant burden. While many dental phenotypes are heritable, most nonsyndromic cases have not been linked to causal genes. We demonstrate the novel finding that common sequence variants associated with human odontogenic phenotypes are enriched in developmental craniofacial enhancers conserved between human and mouse. However, the bulk nature of these samples obscures if this finding is due to the tooth itself or the surrounding tissues. We therefore sought to identify enhancers specifically active in the tooth anlagen and quantify their contribution to the observed genetic enrichments. We systematically identified 22,001 conserved enhancers active in E13.5 mouse incisors using ChIP-seq and machine learning pipelines and demonstrated biologically relevant enrichments in putative target genes, transcription factor binding motifs, and in vivo activity. Multi-tissue comparisons of human and mouse enhancers revealed that these putative tooth enhancers had the strongest enrichment of odontogenic phenotype-associated variants, suggesting a role for dysregulation of tooth developmental enhancers in human dental phenotypes. The large number of these regions genome-wide necessitated prioritization of enhancer loci for future investigations. As enhancers modulate gene expression, we prioritized regions based on enhancers' putative target genes. We predicted these target genes and prioritized loci by integrating chromatin state, bulk gene expression and coexpression, GWAS variants, and cell type resolved gene expression to generate a prioritized list of putative odontogenic phenotype-driving loci active in the developing tooth. These genomic regions are of particular interest for downstream experiments determining the role of specific dental enhancer:gene pairs in odontogenesis.

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Section: Data Integration Prioritizes Dental Enhancers At Predicted O...mentioning

confidence: 99%

Integration of multimodal data in the developing tooth reveals candidate regulatory loci driving human odontogenic phenotypes

Winchester

Cotney

2022

Front. Dent. Med

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“…This framework could potentially be extended to patient level measurements where patients phenotypes as measured with clinical variables and laboratory values can be associated with enriched states in disease or treatment conditions. Indeed MELD has already seen use in several contexts[50][51][52][53][54]. To facilitate the application of these tools for future scRNA-seq analysis, we provide open-source Python implementations that inherit the Scikit-learn API in the MELD package on GitHub https://github.com/KrishnaswamyLab/MELD.…”

mentioning

confidence: 99%

Quantifying the effect of experimental perturbations at single-cell resolution

Burkhardt¹,

Stanley

Tong

et al. 2019

Preprint

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103

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4Single-cell RNA-sequencing (scRNA-seq) is a powerful tool to quantify transcriptional states in 5 thousands to millions of cells. It is increasingly common for scRNA-seq data to be collected in 6 multiple experimental conditions, yet quantifying differences between scRNA-seq datasets re-7 mains an analytical challenge. Previous efforts at quantifying such differences focus on discrete 8 regions of the transcriptional state space such as clusters of cells. Here, we describe a contin-9 uous measure of the effect of an experiment across the transcriptomic space. First, we use the 10 manifold assumption to model the cellular state space as a graph (or network) with cells as nodes 11 and edges connecting cells with similar transcriptomic profiles. Next, we create an Enhanced 12 Experimental Signal (EES) that estimates the likelihood of observing cells from each condition 13 at every point in the manifold. We show that the EES has useful properties and information that 14 can be extracted. The EES can be used to identify how gene expression is affected by a given 15 perturbation, including identifying non-monotonic changes from only two conditions. We also 16 show that we can use both the magnitude and frequency of the EES, using an algorithm we 17 call vertex frequency clustering, to derive subsets of cells at appropriate levels of granularity 18 (tailored to areas that change) that are enriched in the experimental or control conditions or that 19 are unaffected between conditions. We demonstrate both algorithms using a combination of 20 biological and synthetic datasets. Implementations are provided in the MELD Python package, 21 which is available at https://github.com/KrishnaswamyLab/MELD. 22As single-cell RNA-sequencing (scRNA-seq) has become more accessible, the design of single-cell exper-24 iments has become increasingly complex. Researchers regularly use scRNA-seq to quantify the effect of 25 a drug, gene knockout, or other experimental perturbation on a biological system. However, quantifying 26 the compositional differences between single-cell datasets collected from multiple experimental conditions 27 1 remains an analytical challenge [1] because of the heterogeneity and noise in both the data and the effects 28 of a given perturbation. 29 Previous work has shown the utility of modelling the transcriptomic state space as a continuous low-30 dimensional manifold, or set of manifolds, to characterize cellular heterogeneity and dynamic biological 31 processes [2][3][4][5][6][7][8]. In the manifold model, the biologically valid combinations of gene expression are rep-32 resented as a smooth, low-dimensional surface in a high dimensional space, such as a two-dimensional 33 sheet embedded in three dimensions. The main challenge in developing tools to quantify compositional 34 differences between single-cell datasets is that each dataset comprises several intrinsic structures of hetero-35 geneous cells, and the effect of the experimental condition could be diffuse or isolated to particular areas 36 of...

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DIAPH1 Variants in Non–East Asian Patients With Sporadic Moyamoya Disease

et al. 2021

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IMPORTANCE Moyamoya disease (MMD), a progressive vasculopathy leading to narrowing and ultimate occlusion of the intracranial internal carotid arteries, is a cause of childhood stroke. The cause of MMD is poorly understood, but genetic factors play a role. Several familial forms of MMD have been identified, but the cause of most cases remains elusive, especially among non-East Asian individuals. OBJECTIVE To assess whether ultrarare de novo and rare, damaging transmitted variants with large effect sizes are associated with MMD risk. DESIGN, SETTING, AND PARTICIPANTSA genetic association study was conducted using whole-exome sequencing case-parent MMD trios in a small discovery cohort collected over 3.5 years (2016)(2017)(2018)(2019); data were analyzed in 2020. Medical records from US hospitals spanning a range of 1 month to 1.5 years were reviewed for phenotyping. Exomes from a larger validation cohort were analyzed to identify additional rare, large-effect variants in the top candidate gene. Participants included patients with MMD and, when available, their parents. All participants who met criteria and were presented with the option to join the study agreed to do so; none were excluded. Twenty-four probands (22 trios and 2 singletons) composed the discovery cohort, and 84 probands (29 trios and 55 singletons) composed the validation cohort.MAIN OUTCOMES AND MEASURES Gene variants were identified and filtered using stringent criteria. Enrichment and case-control tests assessed gene-level variant burden. In silico modeling estimated the probability of variant association with protein structure. Integrative genomics assessed expression patterns of MMD risk genes derived from single-cell RNA sequencing data of human and mouse brain tissue. RESULTSOf the 24 patients in the discovery cohort, 14 (58.3%) were men and 18 (75.0%) were of European ancestry. Three of 24 discovery cohort probands contained 2 do novo (1-tailed Poisson P = 1.1 × 10 −6 ) and 1 rare, transmitted damaging variant (12.5% of cases) in DIAPH1 (mammalian diaphanous-1), a key regulator of actin remodeling in vascular cells and platelets. Four additional ultrarare damaging heterozygous DIAPH1 variants (3 unphased) were identified in 3 other patients in an 84-proband validation cohort (73.8% female, 77.4% European). All 6 patients were non-East Asian. Compound heterozygous variants were identified in ena/vasodilator-stimulated phosphoproteinlike protein EVL, a mammalian diaphanous-1 interactor that regulates actin polymerization. DIAPH1 and EVL mutant probands had severe, bilateral MMD associated with transfusion-dependent thrombocytopenia. DIAPH1 and other MMD risk genes are enriched in mural cells of midgestational human brain. The DIAPH1 coexpression network converges in vascular cell actin cytoskeleton regulatory pathways.CONCLUSIONS AND RELEVANCE These findings provide the largest collection to date of non-East Asian individuals with sporadic MMD harboring pathogenic variants in the same gene. The results suggest that DIAPH1 is a novel MMD risk...

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Modeling uniquely human gene regulatory function in humanized mice

Cited by 5 publications

References 88 publications

Integration of multimodal data in the developing tooth reveals candidate regulatory loci driving human odontogenic phenotypes

Integration of multimodal data in the developing tooth reveals candidate regulatory loci driving human odontogenic phenotypes

Quantifying the effect of experimental perturbations at single-cell resolution

DIAPH1 Variants in Non–East Asian Patients With Sporadic Moyamoya Disease

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