| Title: |
Organic geochemical evidence for life in Archean rocks identified by pyrolysis–GC–MS and supervised machine learning |
| Authors: |
Wong, Michael L.; Prabhu, Anirudh; Alexander, Conel O’D.; Cleaves II, H. James; Cody, George D.; Hystad, Grethe; Bermanec, Marko; Bleeker, Wouter; Boyce, C. Kevin; Corpolongo, Andrea; Czaja, Andrew D.; Das, Souvik; Gaines, Robert R.; Gregory, Daniel D.; Jaszczak, John A.; Javaux, Emmanuelle J.; Jodder, Jaganmoy; Knoll, Andrew H.; Van Kranendonk, Martin; Maloney, Katie M.; Noffke, Nora; Rainbird, Robert; Slaughter, Emersyn; Stüeken, Eva E.; Summons, Roger E.; Westall, Frances; Wiemann, Jasmina; Xiao, Shuhai; Hazen, Robert M. |
| Contributors: |
University of St Andrews.School of Earth & Environmental Sciences; University of St Andrews.St Andrews Centre for Exoplanet Science |
| Publication Year: |
2025 |
| Collection: |
University of St Andrews: Digital Research Repository |
| Subject Terms: |
Biosignatures; Organic chemistry; Machine learning; Photosynthesis; Meteorites; DAS; MCC |
| Description: |
Funding: Studies of molecular biosignatures have been supported by the John Templeton Foundation Grant 61783. Additional support has been provided by the NASA Astrobiology Institute ENIGMA Team Grant 80NSSC18M0093, a private foundation, and the Carnegie Institution for Science. A.C. and A.D.C. are supported by NSF-Earth Sciences grant 2029521. M.L.W.’s research is funded by NASA through the NASA Hubble Fellowship Program Grant HST-HF2-51521.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. ; Throughout Earth’s history, organic molecules from both abiogenic and biogenic sources have been buried in sedimentary rocks. Most of these organic molecules have been significantly altered by geologic processes through deep time. Nonetheless, the nature and distribution of those ancient fragmentary organic remains have the potential to reveal diagnostic biomolecular information after billions of years of burial. Here, we analyzed 406 fossil, modern biological, meteoritic, and synthetic samples using pyrolysis gas chromatography and mass spectrometry. We explored these analytical data via supervised machine-learning methods to discriminate samples of biogenic vs. abiogenic origin, plant vs. animal phylogenetic affinity, and photosynthetic vs. nonphotosynthetic physiology. Dividing 272 samples with known phylogenetic affinity and physiology into 9 categories, each further divided into 75% training and 25% testing sets, our random forest models accurately predict pairwise assignments of modern vs. fossil or meteoritic organics (100% correct assignments), fossil plant tissues vs. meteoritic organics (97%), modern vs. fossil plant tissues (98%), and modern plants vs. animal tissues (95%). Pairwise comparisons between fossil biogenic samples vs. abiogenic samples resulted in 93% correct classifications, while analysis of modern and ancient photosynthetic vs. nonphotosynthetic samples also resulted in ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| ISBN: |
978-1-05-022231-4; 1-05-022231-8 |
| Relation: |
Proceedings of the National Academy of Sciences of the United States of America; 329109340; 105022231865; Wong, M L, Prabhu, A, Alexander, C OD, Cleaves II, H J, Cody, G D, Hystad, G, Bermanec, M, Bleeker, W, Boyce, C K, Corpolongo, A, Czaja, A D, Das, S, Gaines, R R, Gregory, D D, Jaszczak, J A, Javaux, E J, Jodder, J, Knoll, A H, Van Kranendonk, M, Maloney, K M, Noffke, N, Rainbird, R, Slaughter, E, Stüeken, E E, Summons, R E, Westall, F, Wiemann, J, Xiao, S & Hazen, R M 2025, 'Organic geochemical evidence for life in Archean rocks identified by pyrolysis–GC–MS and supervised machine learning', Proceedings of the National Academy of Sciences of the United States of America, vol. 122, no. 47, e2514534122. https://doi.org/10.1073/pnas.2514534122; crossref: 10.1073/pnas.2514534122; https://hdl.handle.net/10023/33214; https://www.scopus.com/pages/publications/105022231865 |
| DOI: |
10.1073/pnas.2514534122 |
| Availability: |
https://hdl.handle.net/10023/33214; https://doi.org/10.1073/pnas.2514534122; https://www.scopus.com/pages/publications/105022231865 |
| Rights: |
Copyright © 2025 the Author(s). This open access article is distributed under Creative Commons Attribution- NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Accession Number: |
edsbas.240612C9 |
| Database: |
BASE |