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Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis
Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of p...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10565662/ https://www.ncbi.nlm.nih.gov/pubmed/37829445 http://dx.doi.org/10.3389/fmicb.2023.1227300 |
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author | Chang, Che-Cheng Liu, Tzu-Chi Lu, Chi-Jie Chiu, Hou-Chang Lin, Wei-Ning |
author_facet | Chang, Che-Cheng Liu, Tzu-Chi Lu, Chi-Jie Chiu, Hou-Chang Lin, Wei-Ning |
author_sort | Chang, Che-Cheng |
collection | PubMed |
description | Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon–based data and full ASV–based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV–based and ASV taxon–based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG. |
format | Online Article Text |
id | pubmed-10565662 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105656622023-10-12 Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis Chang, Che-Cheng Liu, Tzu-Chi Lu, Chi-Jie Chiu, Hou-Chang Lin, Wei-Ning Front Microbiol Microbiology Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon–based data and full ASV–based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV–based and ASV taxon–based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG. Frontiers Media S.A. 2023-09-27 /pmc/articles/PMC10565662/ /pubmed/37829445 http://dx.doi.org/10.3389/fmicb.2023.1227300 Text en Copyright © 2023 Chang, Liu, Lu, Chiu and Lin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Chang, Che-Cheng Liu, Tzu-Chi Lu, Chi-Jie Chiu, Hou-Chang Lin, Wei-Ning Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis |
title | Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis |
title_full | Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis |
title_fullStr | Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis |
title_full_unstemmed | Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis |
title_short | Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis |
title_sort | machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10565662/ https://www.ncbi.nlm.nih.gov/pubmed/37829445 http://dx.doi.org/10.3389/fmicb.2023.1227300 |
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