hReg-CNCC reconstructs a regulatory network in human cranial neural crest cells and annotates variants in a developmental context

Zhanying Feng, Zhana Duren, Ziyi Xiong, Sijia Wang, Fan Liu*, Wing Hung Wong*, Yong Wang*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

Cranial Neural Crest Cells (CNCC) originate at the cephalic region from forebrain, midbrain and hindbrain, migrate into the developing craniofacial region, and subsequently differentiate into multiple cell types. The entire specification, delamination, migration, and differentiation process is highly regulated and abnormalities during this craniofacial development cause birth defects. To better understand the molecular networks underlying CNCC, we integrate paired gene expression & chromatin accessibility data and reconstruct the genome-wide human Regulatory network of CNCC (hReg-CNCC). Consensus optimization predicts high-quality regulations and reveals the architecture of upstream, core, and downstream transcription factors that are associated with functions of neural plate border, specification, and migration. hReg-CNCC allows us to annotate genetic variants of human facial GWAS and disease traits with associated cis-regulatory modules, transcription factors, and target genes. For example, we reveal the distal and combinatorial regulation of multiple SNPs to core TF ALX1 and associations to facial distances and cranial rare disease. In addition, hReg-CNCC connects the DNA sequence differences in evolution, such as ultra-conserved elements and human accelerated regions, with gene expression and phenotype. hReg-CNCC provides a valuable resource to interpret genetic variants as early as gastrulation during embryonic development. The network resources are available at https://github.com/AMSSwanglab/hReg-CNCC.

Original languageEnglish
Article number442
JournalCommunications Biology
Volume4
Issue number1
DOIs
Publication statusPublished - 6 Apr 2021

Bibliographical note

Funding Information:
This work was supported by Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01), CAS "Light of West China" Program (No.xbzg-zdsys-201913), the "CAS Interdisciplinary Innovation Team" project, National Key R&D Program of China (No. 2017YFC0908400 and 2020YFA0712402), and the National Natural Science Foundation of China (NSFC) under Grants Nos. 12025107, 11871463, 61621003, and 91651507, the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No. XDC01000000 and XDB38010400. Z.X. was supported by China Scholarship Council PhD Fellowship. The computations were partly done by the high-performance computers of State Key Laboratory of Scientific and Engineering Computing, Chinese Academy of Sciences. The work of W.H.W. and D.Z. was supported by NIH grants P50-HG007735 and R01HG010359. We thank reviewers for their insightful suggestions to improve the manuscript.

Publisher Copyright:
© 2021, The Author(s).

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