Structure-Based Modeling of the Gut Bacteria–Host Interactome Through Statistical Analysis of Domain–Domain Associations Using Machine Learning
The gut microbiome, a complex ecosystem of microorganisms, plays a pivotal role in human health and disease. The gut microbiome’s influence extends beyond the digestive system to various organs, and its imbalance is linked to a wide range of diseases, including cancer and neurodevelopmental, inflamm...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | BioTech |
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| Online Access: | https://www.mdpi.com/2673-6284/14/1/13 |
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| author | Despoina P. Kiouri Georgios C. Batsis Thomas Mavromoustakos Alessandro Giuliani Christos T. Chasapis |
| author_facet | Despoina P. Kiouri Georgios C. Batsis Thomas Mavromoustakos Alessandro Giuliani Christos T. Chasapis |
| author_sort | Despoina P. Kiouri |
| collection | DOAJ |
| description | The gut microbiome, a complex ecosystem of microorganisms, plays a pivotal role in human health and disease. The gut microbiome’s influence extends beyond the digestive system to various organs, and its imbalance is linked to a wide range of diseases, including cancer and neurodevelopmental, inflammatory, metabolic, cardiovascular, autoimmune, and psychiatric diseases. Despite its significance, the interactions between gut bacteria and human proteins remain understudied, with less than 20,000 experimentally validated protein interactions between the host and any bacteria species. This study addresses this knowledge gap by predicting a protein–protein interaction network between gut bacterial and human proteins. Using statistical associations between Pfam domains, a comprehensive dataset of over one million experimentally validated pan-bacterial–human protein interactions, as well as inter- and intra-species protein interactions from various organisms, were used for the development of a machine learning-based prediction method to uncover key regulatory molecules in this dynamic system. This study’s findings contribute to the understanding of the intricate gut microbiome–host relationship and pave the way for future experimental validation and therapeutic strategies targeting the gut microbiome interplay. |
| format | Article |
| id | doaj-art-52d59bb452ed485d88902292c0d35afe |
| institution | Kabale University |
| issn | 2673-6284 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioTech |
| spelling | doaj-art-52d59bb452ed485d88902292c0d35afe2025-08-20T03:43:33ZengMDPI AGBioTech2673-62842025-02-011411310.3390/biotech14010013Structure-Based Modeling of the Gut Bacteria–Host Interactome Through Statistical Analysis of Domain–Domain Associations Using Machine LearningDespoina P. Kiouri0Georgios C. Batsis1Thomas Mavromoustakos2Alessandro Giuliani3Christos T. Chasapis4Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, GreeceInstitute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, GreeceLaboratory of Organic Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, 15772 Athens, GreeceEnvironment and Health Department, Istituto Superiore di Sanità, 00161 Rome, ItalyInstitute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, GreeceThe gut microbiome, a complex ecosystem of microorganisms, plays a pivotal role in human health and disease. The gut microbiome’s influence extends beyond the digestive system to various organs, and its imbalance is linked to a wide range of diseases, including cancer and neurodevelopmental, inflammatory, metabolic, cardiovascular, autoimmune, and psychiatric diseases. Despite its significance, the interactions between gut bacteria and human proteins remain understudied, with less than 20,000 experimentally validated protein interactions between the host and any bacteria species. This study addresses this knowledge gap by predicting a protein–protein interaction network between gut bacterial and human proteins. Using statistical associations between Pfam domains, a comprehensive dataset of over one million experimentally validated pan-bacterial–human protein interactions, as well as inter- and intra-species protein interactions from various organisms, were used for the development of a machine learning-based prediction method to uncover key regulatory molecules in this dynamic system. This study’s findings contribute to the understanding of the intricate gut microbiome–host relationship and pave the way for future experimental validation and therapeutic strategies targeting the gut microbiome interplay.https://www.mdpi.com/2673-6284/14/1/13gut microbiomeprotein networksdomain interactionshost–bacteria interactionsmachine learning |
| spellingShingle | Despoina P. Kiouri Georgios C. Batsis Thomas Mavromoustakos Alessandro Giuliani Christos T. Chasapis Structure-Based Modeling of the Gut Bacteria–Host Interactome Through Statistical Analysis of Domain–Domain Associations Using Machine Learning BioTech gut microbiome protein networks domain interactions host–bacteria interactions machine learning |
| title | Structure-Based Modeling of the Gut Bacteria–Host Interactome Through Statistical Analysis of Domain–Domain Associations Using Machine Learning |
| title_full | Structure-Based Modeling of the Gut Bacteria–Host Interactome Through Statistical Analysis of Domain–Domain Associations Using Machine Learning |
| title_fullStr | Structure-Based Modeling of the Gut Bacteria–Host Interactome Through Statistical Analysis of Domain–Domain Associations Using Machine Learning |
| title_full_unstemmed | Structure-Based Modeling of the Gut Bacteria–Host Interactome Through Statistical Analysis of Domain–Domain Associations Using Machine Learning |
| title_short | Structure-Based Modeling of the Gut Bacteria–Host Interactome Through Statistical Analysis of Domain–Domain Associations Using Machine Learning |
| title_sort | structure based modeling of the gut bacteria host interactome through statistical analysis of domain domain associations using machine learning |
| topic | gut microbiome protein networks domain interactions host–bacteria interactions machine learning |
| url | https://www.mdpi.com/2673-6284/14/1/13 |
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