Functional profile of host microbiome indicates Clostridioides difficile infection
Clostridioides difficile infection (CDI) is a gastro-intestinal (GI) infection that illustrates how perturbations in symbiotic host–microbiome interactions render the GI tract vulnerable to the opportunistic pathogens. CDI also serves as an example of how such perturbations could be reversed via gut...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Gut Microbes |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19490976.2022.2135963 |
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| _version_ | 1850140086833774592 |
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| author | Etienne Nzabarushimana Haixu Tang |
| author_facet | Etienne Nzabarushimana Haixu Tang |
| author_sort | Etienne Nzabarushimana |
| collection | DOAJ |
| description | Clostridioides difficile infection (CDI) is a gastro-intestinal (GI) infection that illustrates how perturbations in symbiotic host–microbiome interactions render the GI tract vulnerable to the opportunistic pathogens. CDI also serves as an example of how such perturbations could be reversed via gut microbiota modulation mechanisms, especially fecal microbiota transplantation (FMT). However, microbiome-mediated diagnosis of CDI remains understudied. Here, we evaluated the diagnostic capabilities of the fecal microbiome on the prediction of CDI. We used the metagenomic sequencing data from ten previous studies, encompassing those acquired from CDI patients treated by FMT, CDI-negative patients presenting other intestinal health conditions, and healthy volunteers taking antibiotics. We designed a hybrid species/function profiling approach that determines the abundances of microbial species in the community contributing to its functional profile. These functionally informed taxonomic profiles were then used for classification of the microbial samples. We used logistic regression (LR) models using these features, which showed high prediction accuracy (with an average [Formula: see text]), substantiating that the species/function composition of the gut microbiome has a robust diagnostic prediction of CDI. We further assessed the confounding impact of antibiotic therapy on CDI prediction and found that it is distinguishable from the CDI impact. Finally, we devised a log-odds score computed from the output of the LR models to quantify the likelihood of CDI in a gut microbiome sample and applied it to evaluating the effectiveness of FMT based on post-FMT microbiome samples. The results showed that the gut microbiome of patients exhibited a gradual but steady improvement after receiving successful FMT, indicating the restoration of the normal microbiome functions. |
| format | Article |
| id | doaj-art-a92e96c2e24d4e4383f394c98b2f0621 |
| institution | OA Journals |
| issn | 1949-0976 1949-0984 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Gut Microbes |
| spelling | doaj-art-a92e96c2e24d4e4383f394c98b2f06212025-08-20T02:29:58ZengTaylor & Francis GroupGut Microbes1949-09761949-09842022-12-0114110.1080/19490976.2022.2135963Functional profile of host microbiome indicates Clostridioides difficile infectionEtienne Nzabarushimana0Haixu Tang1Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana, USALuddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana, USAClostridioides difficile infection (CDI) is a gastro-intestinal (GI) infection that illustrates how perturbations in symbiotic host–microbiome interactions render the GI tract vulnerable to the opportunistic pathogens. CDI also serves as an example of how such perturbations could be reversed via gut microbiota modulation mechanisms, especially fecal microbiota transplantation (FMT). However, microbiome-mediated diagnosis of CDI remains understudied. Here, we evaluated the diagnostic capabilities of the fecal microbiome on the prediction of CDI. We used the metagenomic sequencing data from ten previous studies, encompassing those acquired from CDI patients treated by FMT, CDI-negative patients presenting other intestinal health conditions, and healthy volunteers taking antibiotics. We designed a hybrid species/function profiling approach that determines the abundances of microbial species in the community contributing to its functional profile. These functionally informed taxonomic profiles were then used for classification of the microbial samples. We used logistic regression (LR) models using these features, which showed high prediction accuracy (with an average [Formula: see text]), substantiating that the species/function composition of the gut microbiome has a robust diagnostic prediction of CDI. We further assessed the confounding impact of antibiotic therapy on CDI prediction and found that it is distinguishable from the CDI impact. Finally, we devised a log-odds score computed from the output of the LR models to quantify the likelihood of CDI in a gut microbiome sample and applied it to evaluating the effectiveness of FMT based on post-FMT microbiome samples. The results showed that the gut microbiome of patients exhibited a gradual but steady improvement after receiving successful FMT, indicating the restoration of the normal microbiome functions.https://www.tandfonline.com/doi/10.1080/19490976.2022.2135963Functional profilemetagenomicsmicrobiomeClostridioides difficile infectionlogistic regressionmachine learning |
| spellingShingle | Etienne Nzabarushimana Haixu Tang Functional profile of host microbiome indicates Clostridioides difficile infection Gut Microbes Functional profile metagenomics microbiome Clostridioides difficile infection logistic regression machine learning |
| title | Functional profile of host microbiome indicates Clostridioides difficile infection |
| title_full | Functional profile of host microbiome indicates Clostridioides difficile infection |
| title_fullStr | Functional profile of host microbiome indicates Clostridioides difficile infection |
| title_full_unstemmed | Functional profile of host microbiome indicates Clostridioides difficile infection |
| title_short | Functional profile of host microbiome indicates Clostridioides difficile infection |
| title_sort | functional profile of host microbiome indicates clostridioides difficile infection |
| topic | Functional profile metagenomics microbiome Clostridioides difficile infection logistic regression machine learning |
| url | https://www.tandfonline.com/doi/10.1080/19490976.2022.2135963 |
| work_keys_str_mv | AT etiennenzabarushimana functionalprofileofhostmicrobiomeindicatesclostridioidesdifficileinfection AT haixutang functionalprofileofhostmicrobiomeindicatesclostridioidesdifficileinfection |