Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning
Summary: The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host's age based on the gu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-06-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004220303849 |
| _version_ | 1819227131841871872 |
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| author | Fedor Galkin Polina Mamoshina Alex Aliper Evgeny Putin Vladimir Moskalev Vadim N. Gladyshev Alex Zhavoronkov |
| author_facet | Fedor Galkin Polina Mamoshina Alex Aliper Evgeny Putin Vladimir Moskalev Vadim N. Gladyshev Alex Zhavoronkov |
| author_sort | Fedor Galkin |
| collection | DOAJ |
| description | Summary: The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host's age based on the gut community composition. Here we developed a method of predicting hosts' age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers. The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative. |
| first_indexed | 2024-12-23T10:36:31Z |
| format | Article |
| id | doaj.art-35bf281d919a4c76a5b82fe80bd5d563 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-12-23T10:36:31Z |
| publishDate | 2020-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-35bf281d919a4c76a5b82fe80bd5d5632022-12-21T17:50:16ZengElsevieriScience2589-00422020-06-01236101199Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep LearningFedor Galkin0Polina Mamoshina1Alex Aliper2Evgeny Putin3Vladimir Moskalev4Vadim N. Gladyshev5Alex Zhavoronkov6Deep Longevity Inc, Hong Kong Science and Technology Park, Hong Kong; Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UKDeep Longevity Inc, Hong Kong Science and Technology Park, Hong Kong; Insilico Medicine Ltd, Hong Kong Science and Technology Park, Hong KongInsilico Medicine Ltd, Hong Kong Science and Technology Park, Hong KongInsilico Medicine Ltd, Hong Kong Science and Technology Park, Hong KongInsilico Medicine Ltd, Hong Kong Science and Technology Park, Hong KongDivision of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, MA, USADeep Longevity Inc, Hong Kong Science and Technology Park, Hong Kong; Insilico Medicine Ltd, Hong Kong Science and Technology Park, Hong Kong; Buck Institute for Research on Aging, Novato, CA, USA; Biogerontology Research Foundation, London, UK; Corresponding authorSummary: The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host's age based on the gut community composition. Here we developed a method of predicting hosts' age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers. The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative.http://www.sciencedirect.com/science/article/pii/S2589004220303849MicrobiologyMicrobiomeBioinformaticsApplied Computing in Medical ScienceArtificial IntelligenceDeep Learning |
| spellingShingle | Fedor Galkin Polina Mamoshina Alex Aliper Evgeny Putin Vladimir Moskalev Vadim N. Gladyshev Alex Zhavoronkov Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning iScience Microbiology Microbiome Bioinformatics Applied Computing in Medical Science Artificial Intelligence Deep Learning |
| title | Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning |
| title_full | Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning |
| title_fullStr | Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning |
| title_full_unstemmed | Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning |
| title_short | Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning |
| title_sort | human gut microbiome aging clock based on taxonomic profiling and deep learning |
| topic | Microbiology Microbiome Bioinformatics Applied Computing in Medical Science Artificial Intelligence Deep Learning |
| url | http://www.sciencedirect.com/science/article/pii/S2589004220303849 |
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