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Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism
While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial processe...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10345091/ https://www.ncbi.nlm.nih.gov/pubmed/37443184 http://dx.doi.org/10.1038/s41598-023-38228-0 |
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author | Tataru, Christine Peras, Marie Rutherford, Erica Dunlap, Kaiti Yin, Xiaochen Chrisman, Brianna S. DeSantis, Todd Z. Wall, Dennis P. Iwai, Shoko David, Maude M. |
author_facet | Tataru, Christine Peras, Marie Rutherford, Erica Dunlap, Kaiti Yin, Xiaochen Chrisman, Brianna S. DeSantis, Todd Z. Wall, Dennis P. Iwai, Shoko David, Maude M. |
author_sort | Tataru, Christine |
collection | PubMed |
description | While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial processes, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed “cross-omic topics”, which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis. |
format | Online Article Text |
id | pubmed-10345091 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103450912023-07-15 Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism Tataru, Christine Peras, Marie Rutherford, Erica Dunlap, Kaiti Yin, Xiaochen Chrisman, Brianna S. DeSantis, Todd Z. Wall, Dennis P. Iwai, Shoko David, Maude M. Sci Rep Article While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial processes, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed “cross-omic topics”, which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis. Nature Publishing Group UK 2023-07-13 /pmc/articles/PMC10345091/ /pubmed/37443184 http://dx.doi.org/10.1038/s41598-023-38228-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Tataru, Christine Peras, Marie Rutherford, Erica Dunlap, Kaiti Yin, Xiaochen Chrisman, Brianna S. DeSantis, Todd Z. Wall, Dennis P. Iwai, Shoko David, Maude M. Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism |
title | Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism |
title_full | Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism |
title_fullStr | Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism |
title_full_unstemmed | Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism |
title_short | Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism |
title_sort | topic modeling for multi-omic integration in the human gut microbiome and implications for autism |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10345091/ https://www.ncbi.nlm.nih.gov/pubmed/37443184 http://dx.doi.org/10.1038/s41598-023-38228-0 |
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