| Title: | Integrative high-throughput enhancer surveying and functional verification divulges a YY2-condensed regulatory axis conferring risk for osteoporosis |
| Journal: | Cell Genomics |
| Published: | 8 Feb 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38335956/ |
| DOI: | https://doi.org/10.1016/j.xgen.2024.100501 |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 8767
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Abstract
The precise roles of chromatin organization at osteoporosis risk loci remain largely elusive. Here, we combined chromatin interaction conformation (Hi-C) profiling and self-transcribing active regulatory region sequencing (STARR-seq) to qualify enhancer activities of prioritized osteoporosis-associated single-nucleotide polymorphisms (SNPs). We identified 319 SNPs with biased allelic enhancer activity effect (baaSNPs) that linked to hundreds of candidate target genes through chromatin interactions across 146 loci. Functional characterizations revealed active epigenetic enrichment for baaSNPs and prevailing osteoporosis-relevant regulatory roles for their chromatin interaction genes. Further motif enrichment and network mapping prioritized several putative, key transcription factors (TFs) controlling osteoporosis binding to baaSNPs. Specifically, we selected one top-ranked TF and deciphered that an intronic baaSNP (rs11202530) could allele-preferentially bind to YY2 to augment PAPSS2 expression through chromatin interactions and promote osteoblast differentiation. Our results underline the roles of TF-mediated enhancer-promoter contacts for osteoporosis, which may help to better understand the intricate molecular regulatory mechanisms underlying osteoporosis risk loci.</p>
16 Authors
- Xiao-Feng Chen
- Yuan-Yuan Duan
- Ying-Ying Jia
- Qian-Hua Dong
- Wei Shi
- Yan Zhang
- Shan-Shan Dong
- Meng Li
- Zhongbo Liu
- Fei Chen
- Xiao-Ting Huang
- Ruo-Han Hao
- Dong-Li Zhu
- Rui-Hua Jing
- Yan Guo
- Tie-Lin Yang
1 Application
| Application ID | Title |
|---|---|
| 46387 | Integration of regulatory elements and multi-trait genomic and phenomic data to improve the accuracy of risk prediction of human complex diseases/traits |
Enabling scientific discoveries that improve human health