Multiomics integration unravels genotype‐microbiome interactions shaping the conjunctival transcriptome
Abstract Gene expression is a molecular phenotype shaped by the interplay between genotype and environment. The microbiome represents a critical environmental exposure for the host. However, the genotype‐microbiome interactions (GMIs) shaping the host transcriptome remain largely unexplored. Here, w...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | iMetaOmics |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/imo2.37 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832575926167142400 |
|---|---|
| author | Qiaoxing Liang Guo‐Wang Lin Xiaohu Ding Bin Zou Xiaomin Liu Jing Li Yuxin Zhang Xiaofeng Wen Lingyi Liang Jin‐Xin Bei Mingguang He Huijue Jia Lai Wei |
| author_facet | Qiaoxing Liang Guo‐Wang Lin Xiaohu Ding Bin Zou Xiaomin Liu Jing Li Yuxin Zhang Xiaofeng Wen Lingyi Liang Jin‐Xin Bei Mingguang He Huijue Jia Lai Wei |
| author_sort | Qiaoxing Liang |
| collection | DOAJ |
| description | Abstract Gene expression is a molecular phenotype shaped by the interplay between genotype and environment. The microbiome represents a critical environmental exposure for the host. However, the genotype‐microbiome interactions (GMIs) shaping the host transcriptome remain largely unexplored. Here, we integrated conjunctival multiomics data from 120 pairs of twins to investigate GMIs. We identified 272,972 expression quantitative trait loci associated with 5946 genes and 241,073 genotype‐controlled correlations between gene expression and microbial abundance. We developed a modeling approach, MicroGenix, that screens for GMIs from host genome, transcriptome, and microbiome data and identifies GMIs associated with the disease through gene‐based association tests. We applied MicroGenix to the conjunctival data set and found that incorporating GMIs into gene expression prediction models significantly increased prediction accuracy. The genes with increased accuracy were overrepresented by those encoding cell adhesion molecules. We further used MicroGenix to predict the transcriptome from conjunctival metagenomes and identified GMIs associated with ocular surface disease. Our work provides a resource for studying host‐microbiome interactions at the conjunctiva and a computational approach to investigate GMIs using multiomics data. |
| format | Article |
| id | doaj-art-9331ca8adf4f4b1fa108bbd5d720a5d0 |
| institution | Kabale University |
| issn | 2996-9506 2996-9514 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | iMetaOmics |
| spelling | doaj-art-9331ca8adf4f4b1fa108bbd5d720a5d02025-01-31T16:15:24ZengWileyiMetaOmics2996-95062996-95142024-12-0112n/an/a10.1002/imo2.37Multiomics integration unravels genotype‐microbiome interactions shaping the conjunctival transcriptomeQiaoxing Liang0Guo‐Wang Lin1Xiaohu Ding2Bin Zou3Xiaomin Liu4Jing Li5Yuxin Zhang6Xiaofeng Wen7Lingyi Liang8Jin‐Xin Bei9Mingguang He10Huijue Jia11Lai Wei12School of Life Sciences, Greater Bay Area Institute of Precision Medicine (Guangzhou) Fudan University Guangzhou ChinaDepartment of Laboratory Medicine, Zhujiang Hospital Southern Medical University Guangzhou ChinaState Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Guangdong Provincial Clinical Research Center for Ocular Diseases Sun Yat‐sen University Guangzhou ChinaState Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Guangdong Provincial Clinical Research Center for Ocular Diseases Sun Yat‐sen University Guangzhou ChinaBGI Research Wuhan ChinaState Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Guangdong Provincial Clinical Research Center for Ocular Diseases Sun Yat‐sen University Guangzhou ChinaState Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Guangdong Provincial Clinical Research Center for Ocular Diseases Sun Yat‐sen University Guangzhou ChinaSchool of Pharmaceutical Sciences Southern Medical University Guangzhou ChinaState Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Guangdong Provincial Clinical Research Center for Ocular Diseases Sun Yat‐sen University Guangzhou ChinaState Key Laboratory of Oncology in South China, Sun Yat‐sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine Sun Yat‐sen University Guangzhou ChinaState Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Guangdong Provincial Clinical Research Center for Ocular Diseases Sun Yat‐sen University Guangzhou ChinaSchool of Life Sciences, Greater Bay Area Institute of Precision Medicine (Guangzhou) Fudan University Guangzhou ChinaGuangdong Provincial Key Laboratory of Allergy & Clinical Immunology, The Second Affiliated Hospital Guangzhou Medical University Guangzhou ChinaAbstract Gene expression is a molecular phenotype shaped by the interplay between genotype and environment. The microbiome represents a critical environmental exposure for the host. However, the genotype‐microbiome interactions (GMIs) shaping the host transcriptome remain largely unexplored. Here, we integrated conjunctival multiomics data from 120 pairs of twins to investigate GMIs. We identified 272,972 expression quantitative trait loci associated with 5946 genes and 241,073 genotype‐controlled correlations between gene expression and microbial abundance. We developed a modeling approach, MicroGenix, that screens for GMIs from host genome, transcriptome, and microbiome data and identifies GMIs associated with the disease through gene‐based association tests. We applied MicroGenix to the conjunctival data set and found that incorporating GMIs into gene expression prediction models significantly increased prediction accuracy. The genes with increased accuracy were overrepresented by those encoding cell adhesion molecules. We further used MicroGenix to predict the transcriptome from conjunctival metagenomes and identified GMIs associated with ocular surface disease. Our work provides a resource for studying host‐microbiome interactions at the conjunctiva and a computational approach to investigate GMIs using multiomics data.https://doi.org/10.1002/imo2.37cell adhesion moleculesconjunctivagenotype‐microbiome interactionsmachine learningtranscriptome |
| spellingShingle | Qiaoxing Liang Guo‐Wang Lin Xiaohu Ding Bin Zou Xiaomin Liu Jing Li Yuxin Zhang Xiaofeng Wen Lingyi Liang Jin‐Xin Bei Mingguang He Huijue Jia Lai Wei Multiomics integration unravels genotype‐microbiome interactions shaping the conjunctival transcriptome iMetaOmics cell adhesion molecules conjunctiva genotype‐microbiome interactions machine learning transcriptome |
| title | Multiomics integration unravels genotype‐microbiome interactions shaping the conjunctival transcriptome |
| title_full | Multiomics integration unravels genotype‐microbiome interactions shaping the conjunctival transcriptome |
| title_fullStr | Multiomics integration unravels genotype‐microbiome interactions shaping the conjunctival transcriptome |
| title_full_unstemmed | Multiomics integration unravels genotype‐microbiome interactions shaping the conjunctival transcriptome |
| title_short | Multiomics integration unravels genotype‐microbiome interactions shaping the conjunctival transcriptome |
| title_sort | multiomics integration unravels genotype microbiome interactions shaping the conjunctival transcriptome |
| topic | cell adhesion molecules conjunctiva genotype‐microbiome interactions machine learning transcriptome |
| url | https://doi.org/10.1002/imo2.37 |
| work_keys_str_mv | AT qiaoxingliang multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome AT guowanglin multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome AT xiaohuding multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome AT binzou multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome AT xiaominliu multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome AT jingli multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome AT yuxinzhang multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome AT xiaofengwen multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome AT lingyiliang multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome AT jinxinbei multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome AT mingguanghe multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome AT huijuejia multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome AT laiwei multiomicsintegrationunravelsgenotypemicrobiomeinteractionsshapingtheconjunctivaltranscriptome |