A multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis
Abstract Background Eosinophilic esophagitis (EoE) is a chronic immune-mediated rare disease, characterized by esophageal dysfunctions. It is likely to be primarily activated by food antigens and is classified as a chronic disease for most patients. Therefore, a deeper understanding of the pathogene...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-01-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-023-03898-x |
| _version_ | 1828051599712321536 |
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| author | Luca Massimino Alberto Barchi Francesco Vito Mandarino Salvatore Spanò Luigi Antonio Lamparelli Edoardo Vespa Sandro Passaretti Laurent Peyrin-Biroulet Edoardo Vincenzo Savarino Vipul Jairath Federica Ungaro Silvio Danese |
| author_facet | Luca Massimino Alberto Barchi Francesco Vito Mandarino Salvatore Spanò Luigi Antonio Lamparelli Edoardo Vespa Sandro Passaretti Laurent Peyrin-Biroulet Edoardo Vincenzo Savarino Vipul Jairath Federica Ungaro Silvio Danese |
| author_sort | Luca Massimino |
| collection | DOAJ |
| description | Abstract Background Eosinophilic esophagitis (EoE) is a chronic immune-mediated rare disease, characterized by esophageal dysfunctions. It is likely to be primarily activated by food antigens and is classified as a chronic disease for most patients. Therefore, a deeper understanding of the pathogenetic mechanisms underlying EoE is needed to implement and improve therapeutic lines of intervention and ameliorate overall patient wellness. Methods RNA-seq data of 18 different studies on EoE, downloaded from NCBI GEO with faster-qdump ( https://github.com/ncbi/sra-tools ), were batch-corrected and analyzed for transcriptomics and metatranscriptomics profiling as well as biological process functional enrichment. The EoE TaMMA web app was designed with plotly and dash. Tabula Sapiens raw data were downloaded from the UCSC Cell Browser. Esophageal single-cell raw data analysis was performed within the Automated Single-cell Analysis Pipeline. Single-cell data-driven bulk RNA-seq data deconvolution was performed with MuSiC and CIBERSORTx. Multi-omics integration was performed with MOFA. Results The EoE TaMMA framework pointed out disease-specific molecular signatures, confirming its reliability in reanalyzing transcriptomic data, and providing new EoE-specific molecular markers including CXCL14, distinguishing EoE from gastroesophageal reflux disorder. EoE TaMMA also revealed microbiota dysbiosis as a predominant characteristic of EoE pathogenesis. Finally, the multi-omics analysis highlighted the presence of defined classes of microbial entities in subsets of patients that may participate in inducing the antigen-mediated response typical of EoE pathogenesis. Conclusions Our study showed that the complex EoE molecular network may be unraveled through advanced bioinformatics, integrating different components of the disease process into an omics-based network approach. This may implement EoE management and treatment in the coming years. |
| first_indexed | 2024-04-10T19:41:09Z |
| format | Article |
| id | doaj.art-9b478278a4244bc1a852db93013ff577 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-04-10T19:41:09Z |
| publishDate | 2023-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-9b478278a4244bc1a852db93013ff5772023-01-29T12:20:41ZengBMCJournal of Translational Medicine1479-58762023-01-0121111310.1186/s12967-023-03898-xA multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitisLuca Massimino0Alberto Barchi1Francesco Vito Mandarino2Salvatore Spanò3Luigi Antonio Lamparelli4Edoardo Vespa5Sandro Passaretti6Laurent Peyrin-Biroulet7Edoardo Vincenzo Savarino8Vipul Jairath9Federica Ungaro10Silvio Danese11Department of Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San RaffaeleDepartment of Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San RaffaeleDepartment of Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San RaffaeleDepartment of Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San RaffaeleIBD Center, IRCCS Humanitas Research HospitalDepartment of Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San RaffaeleDepartment of Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San RaffaeleInserm NGERE, University of LorraineDepartment of Surgery, Oncology, and Gastroenterology, University of PaduaDepartment of Medicine, Division of Gastroenterology, Western UniversityDepartment of Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San RaffaeleDepartment of Gastroenterology and Digestive Endoscopy, IRCCS Ospedale San RaffaeleAbstract Background Eosinophilic esophagitis (EoE) is a chronic immune-mediated rare disease, characterized by esophageal dysfunctions. It is likely to be primarily activated by food antigens and is classified as a chronic disease for most patients. Therefore, a deeper understanding of the pathogenetic mechanisms underlying EoE is needed to implement and improve therapeutic lines of intervention and ameliorate overall patient wellness. Methods RNA-seq data of 18 different studies on EoE, downloaded from NCBI GEO with faster-qdump ( https://github.com/ncbi/sra-tools ), were batch-corrected and analyzed for transcriptomics and metatranscriptomics profiling as well as biological process functional enrichment. The EoE TaMMA web app was designed with plotly and dash. Tabula Sapiens raw data were downloaded from the UCSC Cell Browser. Esophageal single-cell raw data analysis was performed within the Automated Single-cell Analysis Pipeline. Single-cell data-driven bulk RNA-seq data deconvolution was performed with MuSiC and CIBERSORTx. Multi-omics integration was performed with MOFA. Results The EoE TaMMA framework pointed out disease-specific molecular signatures, confirming its reliability in reanalyzing transcriptomic data, and providing new EoE-specific molecular markers including CXCL14, distinguishing EoE from gastroesophageal reflux disorder. EoE TaMMA also revealed microbiota dysbiosis as a predominant characteristic of EoE pathogenesis. Finally, the multi-omics analysis highlighted the presence of defined classes of microbial entities in subsets of patients that may participate in inducing the antigen-mediated response typical of EoE pathogenesis. Conclusions Our study showed that the complex EoE molecular network may be unraveled through advanced bioinformatics, integrating different components of the disease process into an omics-based network approach. This may implement EoE management and treatment in the coming years.https://doi.org/10.1186/s12967-023-03898-xEsophagusTranscriptomicsWeb appMicrobiota |
| spellingShingle | Luca Massimino Alberto Barchi Francesco Vito Mandarino Salvatore Spanò Luigi Antonio Lamparelli Edoardo Vespa Sandro Passaretti Laurent Peyrin-Biroulet Edoardo Vincenzo Savarino Vipul Jairath Federica Ungaro Silvio Danese A multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis Journal of Translational Medicine Esophagus Transcriptomics Web app Microbiota |
| title | A multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis |
| title_full | A multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis |
| title_fullStr | A multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis |
| title_full_unstemmed | A multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis |
| title_short | A multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis |
| title_sort | multi omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis |
| topic | Esophagus Transcriptomics Web app Microbiota |
| url | https://doi.org/10.1186/s12967-023-03898-x |
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