Environment and taxonomy shape the genomic signature of prokaryotic extremophiles
Abstract This study provides comprehensive quantitative evidence suggesting that adaptations to extreme temperatures and pH imprint a discernible environmental component in the genomic signature of microbial extremophiles. Both supervised and unsupervised machine learning algorithms were used to ana...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-42518-y |
| _version_ | 1797576617603629056 |
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| author | Pablo Millán Arias Joseph Butler Gurjit S. Randhawa Maximillian P. M. Soltysiak Kathleen A. Hill Lila Kari |
| author_facet | Pablo Millán Arias Joseph Butler Gurjit S. Randhawa Maximillian P. M. Soltysiak Kathleen A. Hill Lila Kari |
| author_sort | Pablo Millán Arias |
| collection | DOAJ |
| description | Abstract This study provides comprehensive quantitative evidence suggesting that adaptations to extreme temperatures and pH imprint a discernible environmental component in the genomic signature of microbial extremophiles. Both supervised and unsupervised machine learning algorithms were used to analyze genomic signatures, each computed as the k-mer frequency vector of a 500 kbp DNA fragment arbitrarily selected to represent a genome. Computational experiments classified/clustered genomic signatures extracted from a curated dataset of $$\sim 700$$ ∼ 700 extremophile (temperature, pH) bacteria and archaea genomes, at multiple scales of analysis, $$1\le k \le 6$$ 1 ≤ k ≤ 6 . The supervised learning resulted in high accuracies for taxonomic classifications at $$2\le k \le 6$$ 2 ≤ k ≤ 6 , and medium to medium-high accuracies for environment category classifications of the same datasets at $$3\le k \le 6$$ 3 ≤ k ≤ 6 . For $$k=3$$ k = 3 , our findings were largely consistent with amino acid compositional biases and codon usage patterns in coding regions, previously attributed to extreme environment adaptations. The unsupervised learning of unlabelled sequences identified several exemplars of hyperthermophilic organisms with large similarities in their genomic signatures, in spite of belonging to different domains in the Tree of Life. |
| first_indexed | 2024-03-10T21:55:27Z |
| format | Article |
| id | doaj.art-56fb5a049eb646439b7bb451d9bde39e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T21:55:27Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-56fb5a049eb646439b7bb451d9bde39e2023-11-19T13:09:24ZengNature PortfolioScientific Reports2045-23222023-09-0113111710.1038/s41598-023-42518-yEnvironment and taxonomy shape the genomic signature of prokaryotic extremophilesPablo Millán Arias0Joseph Butler1Gurjit S. Randhawa2Maximillian P. M. Soltysiak3Kathleen A. Hill4Lila Kari5School of Computer Science, University of WaterlooDepartment of Biology, University of Western OntarioSchool of Mathematical and Computational Sciences, University of Prince Edward IslandDepartment of Biology, University of Western OntarioDepartment of Biology, University of Western OntarioSchool of Computer Science, University of WaterlooAbstract This study provides comprehensive quantitative evidence suggesting that adaptations to extreme temperatures and pH imprint a discernible environmental component in the genomic signature of microbial extremophiles. Both supervised and unsupervised machine learning algorithms were used to analyze genomic signatures, each computed as the k-mer frequency vector of a 500 kbp DNA fragment arbitrarily selected to represent a genome. Computational experiments classified/clustered genomic signatures extracted from a curated dataset of $$\sim 700$$ ∼ 700 extremophile (temperature, pH) bacteria and archaea genomes, at multiple scales of analysis, $$1\le k \le 6$$ 1 ≤ k ≤ 6 . The supervised learning resulted in high accuracies for taxonomic classifications at $$2\le k \le 6$$ 2 ≤ k ≤ 6 , and medium to medium-high accuracies for environment category classifications of the same datasets at $$3\le k \le 6$$ 3 ≤ k ≤ 6 . For $$k=3$$ k = 3 , our findings were largely consistent with amino acid compositional biases and codon usage patterns in coding regions, previously attributed to extreme environment adaptations. The unsupervised learning of unlabelled sequences identified several exemplars of hyperthermophilic organisms with large similarities in their genomic signatures, in spite of belonging to different domains in the Tree of Life.https://doi.org/10.1038/s41598-023-42518-y |
| spellingShingle | Pablo Millán Arias Joseph Butler Gurjit S. Randhawa Maximillian P. M. Soltysiak Kathleen A. Hill Lila Kari Environment and taxonomy shape the genomic signature of prokaryotic extremophiles Scientific Reports |
| title | Environment and taxonomy shape the genomic signature of prokaryotic extremophiles |
| title_full | Environment and taxonomy shape the genomic signature of prokaryotic extremophiles |
| title_fullStr | Environment and taxonomy shape the genomic signature of prokaryotic extremophiles |
| title_full_unstemmed | Environment and taxonomy shape the genomic signature of prokaryotic extremophiles |
| title_short | Environment and taxonomy shape the genomic signature of prokaryotic extremophiles |
| title_sort | environment and taxonomy shape the genomic signature of prokaryotic extremophiles |
| url | https://doi.org/10.1038/s41598-023-42518-y |
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