Multi-omic integration of microbiome data for identifying disease-associated modules
Abstract Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-46888-3 |
| _version_ | 1827310102839820288 |
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| author | Efrat Muller Itamar Shiryan Elhanan Borenstein |
| author_facet | Efrat Muller Itamar Shiryan Elhanan Borenstein |
| author_sort | Efrat Muller |
| collection | DOAJ |
| description | Abstract Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing “MintTea”, an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies “disease-associated multi-omic modules”, comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species’ metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions. |
| first_indexed | 2024-04-24T19:54:18Z |
| format | Article |
| id | doaj.art-d59ea67ca66e4362b27d3ec3c49e690c |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-24T19:54:18Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-d59ea67ca66e4362b27d3ec3c49e690c2024-03-24T12:25:58ZengNature PortfolioNature Communications2041-17232024-03-0115111310.1038/s41467-024-46888-3Multi-omic integration of microbiome data for identifying disease-associated modulesEfrat Muller0Itamar Shiryan1Elhanan Borenstein2Blavatnik School of Computer Science, Tel Aviv UniversityBlavatnik School of Computer Science, Tel Aviv UniversityBlavatnik School of Computer Science, Tel Aviv UniversityAbstract Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing “MintTea”, an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies “disease-associated multi-omic modules”, comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species’ metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.https://doi.org/10.1038/s41467-024-46888-3 |
| spellingShingle | Efrat Muller Itamar Shiryan Elhanan Borenstein Multi-omic integration of microbiome data for identifying disease-associated modules Nature Communications |
| title | Multi-omic integration of microbiome data for identifying disease-associated modules |
| title_full | Multi-omic integration of microbiome data for identifying disease-associated modules |
| title_fullStr | Multi-omic integration of microbiome data for identifying disease-associated modules |
| title_full_unstemmed | Multi-omic integration of microbiome data for identifying disease-associated modules |
| title_short | Multi-omic integration of microbiome data for identifying disease-associated modules |
| title_sort | multi omic integration of microbiome data for identifying disease associated modules |
| url | https://doi.org/10.1038/s41467-024-46888-3 |
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