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Looking at the Full Picture: Utilizing Topic Modeling to Determine Disease-Associated Microbiome Communities
The microbiome is a complex micro-ecosystem that provides the host with pathogen defense, food metabolism, and other vital processes. Alterations of the microbiome (dysbiosis) have been linked with a number of diseases such as cancers, multiple sclerosis (MS), Alzheimer’s disease, etc. Generally, di...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401927/ https://www.ncbi.nlm.nih.gov/pubmed/37546903 http://dx.doi.org/10.1101/2023.07.21.549984 |
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author | Shrode, Rachel L. Ollberding, Nicholas J. Mangalam, Ashutosh K. |
author_facet | Shrode, Rachel L. Ollberding, Nicholas J. Mangalam, Ashutosh K. |
author_sort | Shrode, Rachel L. |
collection | PubMed |
description | The microbiome is a complex micro-ecosystem that provides the host with pathogen defense, food metabolism, and other vital processes. Alterations of the microbiome (dysbiosis) have been linked with a number of diseases such as cancers, multiple sclerosis (MS), Alzheimer’s disease, etc. Generally, differential abundance testing between the healthy and patient groups is performed to identify important bacteria (enriched or depleted in one group). However, simply providing a singular species of bacteria to an individual lacking that species for health improvement has not been as successful as fecal matter transplant (FMT) therapy. Interestingly, FMT therapy transfers the entire gut microbiome of a healthy (or mixture of) individual to an individual with a disease. FMTs do, however, have limited success, possibly due to concerns that not all bacteria in the community may be responsible for the healthy phenotype. Therefore, it is important to identify the community of microorganisms linked to the health as well as the disease state of the host. Here we applied topic modeling, a natural language processing tool, to assess latent interactions occurring among microbes; thus, providing a representation of the community of bacteria relevant to healthy vs. disease state. Specifically, we utilized our previously published data that studied the gut microbiome of patients with relapsing-remitting MS (RRMS), a neurodegenerative autoimmune disease that has been linked to a variety of factors, including a dysbiotic gut microbiome. With topic modeling we identified communities of bacteria associated with RRMS, including genera previously discovered, but also other taxa that would have been overlooked simply with differential abundance testing. Our work shows that topic modeling can be a useful tool for analyzing the microbiome in dysbiosis and that it could be considered along with the commonly utilized differential abundance tests to better understand the role of the gut microbiome in health and disease. |
format | Online Article Text |
id | pubmed-10401927 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-104019272023-08-05 Looking at the Full Picture: Utilizing Topic Modeling to Determine Disease-Associated Microbiome Communities Shrode, Rachel L. Ollberding, Nicholas J. Mangalam, Ashutosh K. bioRxiv Article The microbiome is a complex micro-ecosystem that provides the host with pathogen defense, food metabolism, and other vital processes. Alterations of the microbiome (dysbiosis) have been linked with a number of diseases such as cancers, multiple sclerosis (MS), Alzheimer’s disease, etc. Generally, differential abundance testing between the healthy and patient groups is performed to identify important bacteria (enriched or depleted in one group). However, simply providing a singular species of bacteria to an individual lacking that species for health improvement has not been as successful as fecal matter transplant (FMT) therapy. Interestingly, FMT therapy transfers the entire gut microbiome of a healthy (or mixture of) individual to an individual with a disease. FMTs do, however, have limited success, possibly due to concerns that not all bacteria in the community may be responsible for the healthy phenotype. Therefore, it is important to identify the community of microorganisms linked to the health as well as the disease state of the host. Here we applied topic modeling, a natural language processing tool, to assess latent interactions occurring among microbes; thus, providing a representation of the community of bacteria relevant to healthy vs. disease state. Specifically, we utilized our previously published data that studied the gut microbiome of patients with relapsing-remitting MS (RRMS), a neurodegenerative autoimmune disease that has been linked to a variety of factors, including a dysbiotic gut microbiome. With topic modeling we identified communities of bacteria associated with RRMS, including genera previously discovered, but also other taxa that would have been overlooked simply with differential abundance testing. Our work shows that topic modeling can be a useful tool for analyzing the microbiome in dysbiosis and that it could be considered along with the commonly utilized differential abundance tests to better understand the role of the gut microbiome in health and disease. Cold Spring Harbor Laboratory 2023-07-25 /pmc/articles/PMC10401927/ /pubmed/37546903 http://dx.doi.org/10.1101/2023.07.21.549984 Text en https://creativecommons.org/publicdomain/zero/1.0/This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license (https://creativecommons.org/publicdomain/zero/1.0/) . |
spellingShingle | Article Shrode, Rachel L. Ollberding, Nicholas J. Mangalam, Ashutosh K. Looking at the Full Picture: Utilizing Topic Modeling to Determine Disease-Associated Microbiome Communities |
title | Looking at the Full Picture: Utilizing Topic Modeling to Determine Disease-Associated Microbiome Communities |
title_full | Looking at the Full Picture: Utilizing Topic Modeling to Determine Disease-Associated Microbiome Communities |
title_fullStr | Looking at the Full Picture: Utilizing Topic Modeling to Determine Disease-Associated Microbiome Communities |
title_full_unstemmed | Looking at the Full Picture: Utilizing Topic Modeling to Determine Disease-Associated Microbiome Communities |
title_short | Looking at the Full Picture: Utilizing Topic Modeling to Determine Disease-Associated Microbiome Communities |
title_sort | looking at the full picture: utilizing topic modeling to determine disease-associated microbiome communities |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401927/ https://www.ncbi.nlm.nih.gov/pubmed/37546903 http://dx.doi.org/10.1101/2023.07.21.549984 |
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