Evidence map›Paper›PMID 41248278›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

Organic geochemical evidence for life in Archean rocks identified by pyrolysis-GC-MS and supervised machine learning.

Michael L Wong, Anirudh Prabhu, Conel O'D Alexander, H James Cleaves, George D Cody, Grethe Hystad, Marko Bermanec, Wouter Bleeker, C Kevin Boyce, Andrea Corpolongo and 19 more

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

29 authors.

Michael L Wong *Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015.ORCID 0000-0001-8212-3036
Anirudh Prabhu *Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015.ORCID 0000-0002-9921-6084
Conel O'D AlexanderEarth and Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015.ORCID 0000-0002-8558-1427
H James CleavesEarth and Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015.
George D CodyEarth and Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015.ORCID 0000-0003-4176-3648
Grethe HystadDepartment of Mathematics and Statistics, Purdue University Northwest, Hammond, IN 46323.ORCID 0000-0001-9572-1019
Marko BermanecDepartment of Earth Sciences, University of Graz, Graz 8010, Austria.ORCID 0000-0001-6165-3417
Wouter BleekerGeological Survey of Canada, Ottawa, ON K1A 0E8, Canada.ORCID 0000-0002-3783-5840
C Kevin BoyceDepartment of Geological Sciences, Stanford University, Stanford, CA 94305.ORCID 0000-0003-4952-2400
Andrea CorpolongoDepartment of Geosciences, University of Cincinnati, Cincinnati, OH 45221.ORCID 0000-0002-8623-358X
Andrew D CzajaDepartment of Geosciences, University of Cincinnati, Cincinnati, OH 45221.ORCID 0000-0002-2450-0734
Souvik DasState Key Laboratory of Critical Earth Material Cycling and Mineral Deposits, Nanjing University, Nanjing 210023, China.
Robert R GainesGeology Department, Pomona College, Claremont, CA 91711.ORCID 0000-0002-3713-5764
Daniel D GregoryDepartment of Earth Sciences, University of Toronto, Toronto, ON M5S 3B1, Canada.
John A JaszczakA. E. Seaman Mineral Museum and Department of Physics, Michigan Technological University, Houghton, MI 49931.ORCID 0000-0002-1946-2454
Emmanuelle J JavauxDepartment of Geology, University of Liège, Liège 4000, Belgique.ORCID 0000-0002-5272-7610
Jaganmoy JodderDepartment of Geosciences, University of Oslo, Oslo 0316, Norway.ORCID 0000-0003-3993-003X
Andrew H KnollDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138.ORCID 0000-0003-1308-8585
Martin Van KranendonkSchool of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia.ORCID 0000-0002-0611-2703
Katie M MaloneyDepartment of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824.
Nora NoffkeDepartment of Ocean and Earth Sciences, Old Dominion University, Norfolk, VA 23529.
Robert RainbirdGeological Survey of Canada, Ottawa, ON K1A 0E8, Canada.
Emersyn SlaughterDepartment of Geological Sciences, University of Florida, Gainesville, FL 32611.
Eva E StüekenSchool of Earth and Environmental Sciences, University of St. Andrews, St. Andrews KY16 9TS, United Kingdom.
Roger E SummonsDepartment of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139.ORCID 0000-0002-7144-8537
Frances WestallCentre de Biophysique Moléculaire, CNRS-UPR4301, Orléans 45071, France.ORCID 0000-0002-1938-5823
Jasmina WiemannDepartment of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD 21218.
Shuhai XiaoDepartment of Geosciences, Virginia Tech, Blacksburg, VA 24060.ORCID 0000-0003-4655-2663
Robert M HazenEarth and Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015.ORCID 0000-0003-4163-8644

Funding

John Templeton Foundation (JTF) 63158NASA | NASA Astrobiology Institute (NAI) 80NSSC18M0093
6 · The paper itself

Abstract

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 93% correct assignments. Our analyses demonstrate that molecular biosignatures can survive in ancient fossils and allow for the identification of organismal origins and traits. Consistent with previous morphological and isotopic inferences, we present evidence for biogenic molecular assemblages in Paleoarchean rocks (3.33 Ga) and for photoautotrophy in Neoarchean rocks (2.52 Ga).

Indexed as

FossilsGas Chromatography-Mass SpectrometryGeologic SedimentsOrganic ChemicalsSupervised Machine LearningAnimalsPhylogenyPlantsPyrolysisOrganic Chemicalsbiosignaturesmachine learningmeteoritesorganic chemistryphotosynthesis

Identifiers

PMID41248278
PMCPMC12663951

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.