1. Computational and Systems Biology
  2. Developmental Biology

Analysis of zebrafish periderm enhancers facilitates identification of a regulatory variant near human KRT8/18

  1. Huan Liu  Is a corresponding author
  2. Kaylia Duncan
  3. Annika Helverson
  4. Priyanka Kumari
  5. Camille Mumm
  6. Yao Xiao
  7. Jenna Colavincenzo Carlson
  8. Fabrice Darbellay
  9. Axel Visel
  10. Elizabeth Leslie
  11. Patrick Breheny
  12. Albert J Erives
  13. Robert A Cornell  Is a corresponding author
  1. Wuhan University, China
  2. University of Iowa, United States
  3. University of Pittsburgh, United States
  4. Lawrence Berkeley Laboratories, United States
  5. DOE Joint Genome Institute, United States
  6. Emory University School of Medicine, United States
https://doi.org/10.7554/eLife.51325
Share this article
Cite this article
  1. Huan Liu
  2. Kaylia Duncan
  3. Annika Helverson
  4. Priyanka Kumari
  5. Camille Mumm
  6. Yao Xiao
  7. Jenna Colavincenzo Carlson
  8. Fabrice Darbellay
  9. Axel Visel
  10. Elizabeth Leslie
  11. Patrick Breheny
  12. Albert J Erives
  13. Robert A Cornell
(2020)
Analysis of zebrafish periderm enhancers facilitates identification of a regulatory variant near human KRT8/18
eLife 9:e51325.
https://doi.org/10.7554/eLife.51325
  2. Download BibTeX
  3. Download .RIS

Abstract

Genome wide association studies for non-syndromic orofacial cleft (OFC) have identified single nucleotide polymorphisms (SNPs) at loci where the presumed risk-relevant gene is expressed in oral periderm. The functional subsets of such SNPs are difficult to predict because the sequence underpinnings of periderm enhancers are unknown. We applied ATAC-seq to models of human palate periderm, including zebrafish periderm, mouse embryonic palate epithelia, and a human oral epithelium cell line, and to complementary mesenchymal cell types. We identified sets of enhancers specific to the epithelial cells and trained gapped-kmer support-vector-machine classifiers on these sets. We used the classifiers to predict the effect of 14 OFC-associated SNPs at 12q13 near KRT18. All the classifiers picked the same SNP as having the strongest effect, but the significance was highest with the classifier trained on zebrafish periderm. Reporter and deletion analyses support this SNP as lying within a periderm enhancer regulating KRT18/KRT8 expression.

Data availability

1 Raw and processed sequencing data were deposited in GEO repository (GSE140241, GSE139945 and GSE139809).2 Custom scripts and piplines we deployed for sequencing data analysis and visualization are available at https://github.com/Badgerliu/periderm_ATACSeq.3 All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Huan Liu

    State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory for Oral Biomedicine of Ministry of Education (KLOBM), School and Hospital of Stomatology, Wuhan University, Wuhan, China
    For correspondence
    liu.huan@whu.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9947-6687

  2. Kaylia Duncan

    College of Medicine, University of Iowa, Iowa City, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Annika Helverson

    College of Medicine, University of Iowa, Iowa City, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Priyanka Kumari

    College of Medicine, University of Iowa, Iowa City, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Camille Mumm

    College of Medicine, University of Iowa, Iowa City, United States
    Competing interests
    The authors declare that no competing interests exist.

  6. Yao Xiao

    State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory for Oral Biomedicine of Ministry of Education (KLOBM), School and Hospital of Stomatology, Wuhan University, Wuhan, China
    Competing interests
    The authors declare that no competing interests exist.

  7. Jenna Colavincenzo Carlson

    Department of Biostatistics, University of Pittsburgh, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5483-0833

  8. Fabrice Darbellay

    Environmental Genomics and Systems Biology Division, Lawrence Berkeley Laboratories, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.

  9. Axel Visel

    DOE Joint Genome Institute, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4130-7784

  10. Elizabeth Leslie

    Department of Human Genetics, Emory University School of Medicine, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.

  11. Patrick Breheny

    Department of Biostatistics, University of Iowa, Iowa City, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0650-1119

  12. Albert J Erives

    Department of Biology, University of Iowa, Iowa City, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7107-5518

  13. Robert A Cornell

    College of Medicine, University of Iowa, Iowa City, United States
    For correspondence
    robert-cornell@uiowa.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4207-9100

Funding

National Institutes of Health (DE023575)

  • Robert A Cornell

National Institute for Health Research (DE027362)

  • Robert A Cornell

National Institute of Dental and Craniofacial Research (DE025060)

  • Elizabeth Leslie

National Institute of Dental and Craniofacial Research (DE024427)

  • Axel Visel

National Institute of Dental and Craniofacial Research (DE028599)

  • Axel Visel

National Natural Science Foundation of China (81771057)

  • Huan Liu

National Natural Science Foundation of China (81400477)

  • Huan Liu

Natural Science Foundation of Hubei Province (2017CFB515)

  • Huan Liu

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: D. rerio were maintained in the University of Iowa Animal Care Facility according to a standard protocol (protocol no. 6011616). All mouse experiments were performed in accordance with approval of the Institutional Animal Care and Use Committees at the School and Hospital of Stomatology of Wuhan University (protocol no. 00271454).Mouse experiments for LacZ reporter transgenic animal work performed at the Lawrence Berkeley National Laboratory (LBNL) were reviewed and approved by the LBNL Animal Welfare and Research Committee.

Copyright

© 2020, Liu et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 3,255
    views
  • 404
    downloads
  • 26
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Huan Liu
  2. Kaylia Duncan
  3. Annika Helverson
  4. Priyanka Kumari
  5. Camille Mumm
  6. Yao Xiao
  7. Jenna Colavincenzo Carlson
  8. Fabrice Darbellay
  9. Axel Visel
  10. Elizabeth Leslie
  11. Patrick Breheny
  12. Albert J Erives
  13. Robert A Cornell
(2020)
Analysis of zebrafish periderm enhancers facilitates identification of a regulatory variant near human KRT8/18
eLife 9:e51325.
https://doi.org/10.7554/eLife.51325

Share this article

https://doi.org/10.7554/eLife.51325

Categories and tags

Research organisms

Further reading

    1. Computational and Systems Biology

    A model-based factorization method for scRNA data unveils bifurcating transcriptional modules underlying cell fate determination

    Jun Ren, Ying Zhou ... Qiyuan Li
    Research Article

    Manifold-learning is particularly useful to resolve the complex cellular state space from single-cell RNA sequences. While current manifold-learning methods provide insights into cell fate by inferring graph-based trajectory at cell level, challenges remain to retrieve interpretable biology underlying the diverse cellular states. Here, we described MGPfactXMBD, a model-based manifold-learning framework and capable to factorize complex development trajectories into independent bifurcation processes of gene sets, and thus enables trajectory inference based on relevant features. MGPfactXMBD offers a more nuanced understanding of the biological processes underlying cellular trajectories with potential determinants. When bench-tested across 239 datasets, MGPfactXMBD showed advantages in major quantity-control metrics, such as branch division accuracy and trajectory topology, outperforming most established methods. In real datasets, MGPfactXMBD recovered the critical pathways and cell types in microglia development with experimentally valid regulons and markers. Furthermore, MGPfactXMBD discovered evolutionary trajectories of tumor-associated CD8+ T cells and yielded new subtypes of CD8+ T cells with gene expression signatures significantly predictive of the responses to immune checkpoint inhibitor in independent cohorts. In summary, MGPfactXMBD offers a manifold-learning framework in scRNA-seq data which enables feature selection for specific biological processes and contributing to advance our understanding of biological determination of cell fate.

    1. Computational and Systems Biology
    2. Genetics and Genomics

    Risk factors affecting polygenic score performance across diverse cohorts

    Daniel Hui, Scott Dudek ... Marylyn D Ritchie
    Research Article

    Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI–covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

    1. Computational and Systems Biology
    2. Neuroscience

    Multisensory integration operates on correlated input from unimodal transient channels

    Cesare V Parise, Marc O Ernst
    Research Article

    Audiovisual information reaches the brain via both sustained and transient input channels, representing signals’ intensity over time or changes thereof, respectively. To date, it is unclear to what extent transient and sustained input channels contribute to the combined percept obtained through multisensory integration. Based on the results of two novel psychophysical experiments, here we demonstrate the importance of the transient (instead of the sustained) channel for the integration of audiovisual signals. To account for the present results, we developed a biologically inspired, general-purpose model for multisensory integration, the multisensory correlation detectors, which combines correlated input from unimodal transient channels. Besides accounting for the results of our psychophysical experiments, this model could quantitatively replicate several recent findings in multisensory research, as tested against a large collection of published datasets. In particular, the model could simultaneously account for the perceived timing of audiovisual events, multisensory facilitation in detection tasks, causality judgments, and optimal integration. This study demonstrates that several phenomena in multisensory research that were previously considered unrelated, all stem from the integration of correlated input from unimodal transient channels.