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Liu X, Chen M, Qu X, Liu W, Dou Y, Liu Q, Shi D, Jiang M, Li H. Cis-Regulatory Elements in Mammals. Int J Mol Sci 2023; 25:343. [PMID: 38203513 PMCID: PMC10779164 DOI: 10.3390/ijms25010343] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024] Open
Abstract
In cis-regulatory elements, enhancers and promoters with complex molecular interactions are used to coordinate gene transcription through physical proximity and chemical modifications. These processes subsequently influence the phenotypic characteristics of an organism. An in-depth exploration of enhancers and promoters can substantially enhance our understanding of gene regulatory networks, shedding new light on mammalian development, evolution and disease pathways. In this review, we provide a comprehensive overview of the intrinsic structural attributes, detection methodologies as well as the operational mechanisms of enhancers and promoters, coupled with the relevant novel and innovative investigative techniques used to explore their actions. We further elucidated the state-of-the-art research on the roles of enhancers and promoters in the realms of mammalian development, evolution and disease, and we conclude with forward-looking insights into prospective research avenues.
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Affiliation(s)
| | | | | | | | | | | | | | - Mingsheng Jiang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning 530005, China
| | - Hui Li
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi Key Laboratory of Animal Breeding, Disease Control and Prevention, College of Animal Science and Technology, Guangxi University, Nanning 530005, China
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Feng Z, Duren Z, Xin J, Yuan Q, He Y, Su B, Wong WH, Wang Y. Heritability enrichment in context-specific regulatory networks improves phenotype-relevant tissue identification. eLife 2022; 11:82535. [PMID: 36525361 PMCID: PMC9810332 DOI: 10.7554/elife.82535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Systems genetics holds the promise to decipher complex traits by interpreting their associated SNPs through gene regulatory networks derived from comprehensive multi-omics data of cell types, tissues, and organs. Here, we propose SpecVar to integrate paired chromatin accessibility and gene expression data into context-specific regulatory network atlas and regulatory categories, conduct heritability enrichment analysis with genome-wide association studies (GWAS) summary statistics, identify relevant tissues, and estimate relevance correlation to depict common genetic factors acting in the shared regulatory networks between traits. Our method improves power upon existing approaches by associating SNPs with context-specific regulatory elements to assess heritability enrichments and by explicitly prioritizing gene regulations underlying relevant tissues. Ablation studies, independent data validation, and comparison experiments with existing methods on GWAS of six phenotypes show that SpecVar can improve heritability enrichment, accurately detect relevant tissues, and reveal causal regulations. Furthermore, SpecVar correlates the relevance patterns for pairs of phenotypes and better reveals shared SNP-associated regulations of phenotypes than existing methods. Studying GWAS of 206 phenotypes in UK Biobank demonstrates that SpecVar leverages the context-specific regulatory network atlas to prioritize phenotypes' relevant tissues and shared heritability for biological and therapeutic insights. SpecVar provides a powerful way to interpret SNPs via context-specific regulatory networks and is available at https://github.com/AMSSwanglab/SpecVar, copy archived at swh:1:rev:cf27438d3f8245c34c357ec5f077528e6befe829.
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Affiliation(s)
- Zhanying Feng
- CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina
- School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of SciencesBeijingChina
| | - Zhana Duren
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson UniversityGreenwoodUnited States
| | - Jingxue Xin
- Department of Statistics, Department of Biomedical Data Science, Bio-X Program, Stanford UniversityStanfordUnited States
| | - Qiuyue Yuan
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson UniversityGreenwoodUnited States
| | - Yaoxi He
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Bing Su
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of SciencesKunmingChina
| | - Wing Hung Wong
- Department of Statistics, Department of Biomedical Data Science, Bio-X Program, Stanford UniversityStanfordUnited States
| | - Yong Wang
- CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina
- School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of SciencesBeijingChina
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of SciencesKunmingChina
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of SciencesHangzhouChina
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Microbial Biogeography along the Gastrointestinal Tract Segments of Sympatric Subterranean Rodents ( Eospalax baileyi and Eospalax cansus). Animals (Basel) 2021; 11:ani11113297. [PMID: 34828028 PMCID: PMC8614254 DOI: 10.3390/ani11113297] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/13/2021] [Accepted: 11/16/2021] [Indexed: 01/04/2023] Open
Abstract
Simple Summary The gut microbiota are crucial for hosts. For mammals, different gastrointestinal tract (GIT) segments have specific microbial communities, which play an essential role in the host’s nutrition, metabolism, immunity, and health. Plateau zokors (Eospalax baileyi) and Gansu zokors (Eospalax cansus) are closely related species that belong to the Spalacidae family, and are common pests in agriculture, forestry, and animal husbandry in northwestern China, with a sympatric distribution area in the transition zone between the Qinghai-Tibetan Plateau and the Loess Plateau. Here, the characteristics of the microbiota communities in different GIT segments of the plateau zokor and the Gansu zokor were studied, and the microbiota communities of the two zokor species were compared. Our results provide important information for further study on the function of microbiota communities in different GIT segments and the potential use of the gut microbiota as a new method for the population management of the zokors. Abstract In this study, based on high-throughput sequencing technology, the biodiversity and the community structure of microbiota in different GIT segments (the stomach, small intestine, cecum and rectum) of plateau zokors and Gansu zokors were studied and compared. A source tracking analysis for the microbial communities of different GIT segments was carried out using the fast expectation–maximization microbial source tracking (FEAST) method. We found that, for both species, the microbial community richness and diversity of the small intestine were almost the lowest while those of the cecum were the highest among the four segments of the GIT. Beta diversity analyses revealed that the bacterial community structures of different GIT segments were significantly different. As for the comparison between species, the bacterial community compositions of the whole GIT, as well as for each segment, were all significantly different. Source tracking conducted on both zokors indicated that the soil has little effect on the bacterial community of the GIT. A fairly high percentage of rectum source for the bacterial community of the stomach indicated that both zokors may engage in coprophagy.
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Feng Z, Duren Z, Xiong Z, Wang S, Liu F, Wong WH, Wang Y. hReg-CNCC reconstructs a regulatory network in human cranial neural crest cells and annotates variants in a developmental context. Commun Biol 2021; 4:442. [PMID: 33824393 PMCID: PMC8024315 DOI: 10.1038/s42003-021-01970-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 03/09/2021] [Indexed: 12/19/2022] Open
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 .
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Affiliation(s)
- Zhanying Feng
- CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, China.,School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China
| | - Zhana Duren
- Center for Human Genetics, Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, USA.,Department of Statistics, Department of Biomedical Data Science, Bio-X Program, Stanford University, Stanford, CA, USA
| | - Ziyi Xiong
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands.,Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands.,CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Sijia Wang
- Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Fan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China. .,China National Center for Bioinformation, Chinese Academy of Sciences, Beijing, China.
| | - Wing Hung Wong
- Department of Statistics, Department of Biomedical Data Science, Bio-X Program, Stanford University, Stanford, CA, USA.
| | - Yong Wang
- CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, China. .,School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China. .,Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China.
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Liu D, Song P, Yan J, Wang H, Cai Z, Xie J, Zhang T. Gut Microbiome Changes in Captive Plateau Zokors ( Eospalax baileyi). Evol Bioinform Online 2021; 17:1176934321996353. [PMID: 34103885 PMCID: PMC8164558 DOI: 10.1177/1176934321996353] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 01/27/2021] [Indexed: 12/28/2022] Open
Abstract
Wild-caught animals must cope with drastic lifestyle and dietary changes after being induced to captivity. How the gut microbiome structure of these animals will change in response receives increasing attention. The plateau zokor (Eospalax baileyi), a typic subterranean rodent endemic to the Qinghai-Tibet plateau, spends almost the whole life underground and is well adapted to the environmental pressures of both plateau and underground. However, how the gut microbiome of the plateau zokor will change in response to captivity has not been reported to date. This study compared the microbial community structure and functions of 22 plateau zokors before (the WS group) and after being kept in captivity for 15 days (the LS group, fed on carrots) using the 16S rRNA gene via high-throughput sequencing technology. The results showed that the LS group retained 973 of the 977 operational taxonomic units (OTUs) in the WS group, and no new OTUs were found in the LS group. The dominant bacterial phyla were Bacteroides and Firmicutes in both groups. In alpha diversity analysis, the Shannon, Sobs, and ACE indexes of the LS group were significantly lower than those of the WS group. A remarkable difference (P < 0.01) between groups was also detected in beta diversity analysis. The UPGMA clustering, NMDS, PCoA, and Anosim results all showed that the intergroup difference was significantly greater than the intragroup difference. And compared with the WS group, the intragroup difference of the gut microbiota in the LS group was much larger, which failed to support the assumption that similar diets should drive convergence of gut microbial communities. PICRUSt revealed that although some functional categories displayed significant differences between groups, the relative abundances of these categories were very close in both groups. Based on all the results, we conclude that as plateau zokors enter captivity for a short time, although the relative abundances of different gut microbiota categories shifted significantly, they can maintain almost all the OTUs and the functions of the gut microbiota in the wild. So, the use of wild-caught plateau zokors in gut microbial studies is acceptable if the time in captivity is short.
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Affiliation(s)
- Daoxin Liu
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, China.,Kunlun College of Qinghai University, Xining, Qinghai, China.,Qinghai Provincial Key Laboratory of Animal Ecological Genomics, Xining, Qinghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Pengfei Song
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, China.,Qinghai Provincial Key Laboratory of Animal Ecological Genomics, Xining, Qinghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jingyan Yan
- College of Agriculture and Animal Husbandry, Qinghai University, Xining, Qinghai, China
| | - Haijing Wang
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, China.,Qinghai Provincial Key Laboratory of Animal Ecological Genomics, Xining, Qinghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Zhenyuan Cai
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, China.,Qinghai Provincial Key Laboratory of Animal Ecological Genomics, Xining, Qinghai, China
| | - Jiuxiang Xie
- College of Agriculture and Animal Husbandry, Qinghai University, Xining, Qinghai, China
| | - Tongzuo Zhang
- Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, China.,Qinghai Provincial Key Laboratory of Animal Ecological Genomics, Xining, Qinghai, China
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