Annotation-free prediction of microbial dioxygen utilization
Creators
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1.
California Institute of Technology
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2.
University of California, San Diego
Abstract
Aerobes require dioxygen (O2) to grow; anaerobes do not. But nearly all microbes — aerobes, anaerobes, and facultative organisms alike — express enzymes whose substrates include O2, if only for detoxification. This presents a challenge when trying to assess which organisms are aerobic from genomic data alone. This challenge can be overcome by noting that O2 utilization has wide-ranging effects on microbes: aerobes typically have larger genomes, encode more O2-utilizing enzymes, and tend to use different amino acids in their proteins. Here we show that these effects permit high-quality prediction of O2 utilization from genome sequences, with several models displaying >70% balanced accuracy on a ternary classification task wherein blind guessing is only 33.3% accurate. Since genome annotation is compute-intensive and relies on many assumptions, we asked if annotation-free methods also perform well. We discovered that simple and efficient models based entirely on genome sequence content — e.g. triplets of amino acids — perform about as well as intensive annotation-based algorithms, enabling the rapid processing of global-scale sequence data to predict aerobic physiology. To demonstrate the utility of efficient physiological predictions we estimated the prevalence of aerobes and anaerobes along a well-studied O2 gradient in the Black Sea, finding strong quantitative correspondence between local chemistry (O2:sulfide concentration ratio) and the composition of microbial communities. We therefore suggest that statistical methods like ours can be used to estimate, or “sense,” pivotal features of the environment from DNA sequencing data.
Copyright and License
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Acknowledgement
The authors thank Daniel Dar, Jagoda Jabłońska, Ranjani Murali, and Jared Leadbetter for valuable discussions. This research was supported in part by the National Science Foundation under Grant No. NSF PHY-1748958. A.I.F was supported by the Jane Coffin Childs Memorial Fund for Medical Research. J.E.G. was supported by the Gordon and Betty Moore Foundation as Physics of Living Systems Fellows through grant number GBMF4513. A.J. acknowledges support from the Howard Hughes Medical Institute as a Hanna Gray Fellow (Grant #GT16787) and from the National Institute of Health through the UCSD FIRST program. W.W.F. acknowledges support from the Resnick Sustainability Institute, the Caltech Center for Evolutionary Sciences, and NSF NNA grant 2127442. This research was also sponsored by the Army Research Office and was accomplished under the Cooperative Agreement Number W911NF-22-2-0210 to D.K.N.
Contributions
A.I.F., J.E.G, A.J., W.W.F. and D.K.N. designed the research. J.E.G., A.I.F., E.M.L., and A.J. wrote code and performed analysis. A.I.F. and J.E.G. wrote the manuscript with approval of all authors.
Data Availability
Code and data are available on the following github repository: https://github.com/jgoldford/aerobot. Training data can be downloaded from Google Cloud using the provided download_training_data() function.
Files
2024.01.16.575888v1.full.pdf
Additional details
Additional titles
- Alternative title (English)
- Predicting aerobic physiology from genomes
Related works
- Is previous version of
- Journal Article: 10.1128/msystems.00763-24 (DOI)
Funding
- National Science Foundation
- - PHY-1748958
- Jane Coffin Childs Memorial Fund for Medical Research
- Gordon and Betty Moore Foundation
- Physics of Living Systems Fellows GBMF4513
- Howard Hughes Medical Institute
- Hanna Gray Fellow GT16787
- National Institutes of Health
- UCSD FIRST -
- California Institute of Technology
- Caltech Center for Evolutionary Sciences -
- National Science Foundation
- Navigating the New Arctic (NNA) 2127442
- United States Army Research Office
- W911NF-22-2-0210
- Resnick Sustainability Institute
Caltech Custom Metadata
- Caltech groups
- Division of Biology and Biological Engineering (BBE)
- Publication Status
- Submitted